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83f4f0edc33f0cc6221e922ad5ca3bc27a676eb4e66cd83492e6323d72001df8 | 2026-01-21T00:00:00-05:00 | AI Skills Improve Job Prospects: Causal Evidence from a Hiring Experiment | arXiv:2601.13286v1 Announce Type: new Abstract: The growing adoption of artificial intelligence (AI) technologies has heightened interest in the labour market value of AI-related skills, yet causal evidence on their role in hiring decisions remains scarce. This study examines whether AI skills serve as a positive hirin... | https://arxiv.org/abs/2601.13286 | Academic Papers | svg |
f2ba8a2b9b833e72db9bf62c8b6194a53b41332baab2f7fcf27bf5dfdaa5af6a | 2026-01-21T00:00:00-05:00 | Human-AI Collaboration in Radiology: The Case of Pulmonary Embolism | arXiv:2601.13379v1 Announce Type: new Abstract: We study how radiologists use AI to diagnose pulmonary embolism (PE), tracking over 100,000 scans interpreted by nearly 400 radiologists during the staggered rollout of a real-world FDA-approved diagnostic platform in a hospital system. When AI flags PE, radiologists agre... | https://arxiv.org/abs/2601.13379 | Academic Papers | svg |
7c838cf9e8d7acfc4cdaff976d19c1950ae981eb3984d58e778e6e5fdd033a80 | 2026-01-21T00:00:00-05:00 | Accelerator and Brake: Dynamic Persuasion with Dead Ends | arXiv:2601.13686v1 Announce Type: new Abstract: We study optimal dynamic persuasion in a bandit experimentation model where a principal, unlike in standard settings, has a single-peaked preference over the agent's stopping time. This non-monotonic preference arises because maximizing the agent's effort is not always in... | https://arxiv.org/abs/2601.13686 | Academic Papers | svg |
4d5eda05e5275f42f5de8619123117bd04b74ba40f55a4c15236eb2e4d0b4ca4 | 2026-01-21T00:00:00-05:00 | Liabilities for the social cost of carbon | arXiv:2601.13834v1 Announce Type: new Abstract: We estimate the national social cost of carbon using a recent meta-analysis of the total impact of climate change and a standard integrated assessment model. The average social cost of carbon closely follows per capita income, the national social cost of carbon the size o... | https://arxiv.org/abs/2601.13834 | Academic Papers | svg |
c266c5c089b0b2674b95f509296aeb8de6012d4e56f239ffdf8c50c6c56cb103 | 2026-01-21T00:00:00-05:00 | How Disruptive is Financial Technology? | arXiv:2601.14071v1 Announce Type: new Abstract: We study whether Fintech disrupts the banking sector by intensifying competition for scarce deposits funds and raising deposit rates. Using difference-in-difference estimation around the exogenous removal of marketplace platform investing restrictions by US states, we sho... | https://arxiv.org/abs/2601.14071 | Academic Papers | svg |
df6cd9f539497fadda29c41b19fb87373ed610d1e043671280146903549ea62d | 2026-01-21T00:00:00-05:00 | Hot Days, Unsafe Schools? The Impact of Heat on School Shootings | arXiv:2601.14094v1 Announce Type: new Abstract: Using data on school shooting incidents in U.S. K--12 schools from 1981 to 2022, we estimate the causal effects of high temperatures on school shootings and assess the implications of climate change. We find that days with maximum temperatures exceeding 90$^\circ$F lead t... | https://arxiv.org/abs/2601.14094 | Academic Papers | svg |
8f465e4299b627a0fc3bb45cb4dcdcb4c8292441b91da8862ed5b44e185fe13e | 2026-01-21T00:00:00-05:00 | Foreign influencer operations: How TikTok shapes American perceptions of China | arXiv:2601.14118v1 Announce Type: new Abstract: How do authoritarian regimes strengthen global support for nondemocratic political systems? Roughly half of the users of the social media platform TikTok report getting news from social media influencers. Against this backdrop, authoritarian regimes have increasingly outs... | https://arxiv.org/abs/2601.14118 | Academic Papers | svg |
8799a862a4d507ff4f3a32bd91c8d4d6c78504819d331859f68fb4df58914817 | 2026-01-21T00:00:00-05:00 | Trade relationships during and after a crisis | arXiv:2601.14150v1 Announce Type: new Abstract: I study how firms adjust to temporary disruptions in international trade relationships organized through relational contracts. I exploit an extreme, plausibly exogenous weather shock during the 2010-11 La Ni\~na season that restricted Colombian flower exporters' access to... | https://arxiv.org/abs/2601.14150 | Academic Papers | svg |
2d8e6855b00f8904f532619f480d879a37cedf74456ec24ce3f4378266f2cfff | 2026-01-21T00:00:00-05:00 | Settling the Score: Portioning with Cardinal Preferences | arXiv:2307.15586v5 Announce Type: cross Abstract: We study a portioning setting in which a public resource such as time or money is to be divided among a given set of candidates, and each agent proposes a division of the resource. We consider two families of aggregation rules for this setting -- those based on coordina... | https://arxiv.org/abs/2307.15586 | Academic Papers | svg |
da125a1cd7bc50b928ad8d623d399214a06146a3411ae783ec7dffc0dc277369 | 2026-01-21T00:00:00-05:00 | Latent Variable Phillips Curve | arXiv:2601.11601v1 Announce Type: cross Abstract: This paper re-examines the empirical Phillips curve (PC) model and its usefulness in the context of medium-term inflation forecasting. A latent variable Phillips curve hypothesis is formulated and tested using 3,968 randomly generated factor combinations. Evidence from ... | https://arxiv.org/abs/2601.11601 | Academic Papers | svg |
0c7ddb5d67f401021dcf377a3653863212074c93bc47a0284345f71cd6ef996e | 2026-01-21T00:00:00-05:00 | Conservation priorities to prevent the next pandemic | arXiv:2601.13349v1 Announce Type: cross Abstract: Diseases originating from wildlife pose a significant threat to global health, causing human and economic losses each year. The transmission of disease from animals to humans occurs at the interface between humans, livestock, and wildlife reservoirs, influenced by abiot... | https://arxiv.org/abs/2601.13349 | Academic Papers | svg |
54f539cc9c3b23857e9fc1d7ab4cabd12308d38add968b0176e1cb8e4effc872 | 2026-01-21T00:00:00-05:00 | On the Anchoring Effect of Monetary Policy on the Labor Share of Income and the Rationality of Its Setting Mechanism | arXiv:2601.13675v1 Announce Type: cross Abstract: Modern macroeconomic monetary theory suggests that the labor share of income has effectively become a core macroe-conomic parameter anchored by top policymakers through Open Market Operations (OMO). However, the setting of this parameter remains a subject of intense eco... | https://arxiv.org/abs/2601.13675 | Academic Papers | svg |
20866f3ad41f194b7f9ab225512cbbde1f34e11a6e761886fbb2b48bea707a1c | 2026-01-21T00:00:00-05:00 | A simple model of interbank trading with tiered remuneration | arXiv:2006.10946v2 Announce Type: replace Abstract: Many countries have adopted negative interest rate policies with tiering remuneration, which allows for exemption from negative rates. This practice has led to higher interbank trading volumes, with market rates ranging between zero and the negative remuneration rates... | https://arxiv.org/abs/2006.10946 | Academic Papers | svg |
a0fc5204af3aa1a8388e447d4a6e5ac81df9b4ccde5099be120649dcd1d47635 | 2026-01-21T00:00:00-05:00 | Market-Based Asset Price Probability | arXiv:2205.07256v5 Announce Type: replace Abstract: The random values and volumes of consecutive trades made at the exchange with shares of security determine its mean, variance, and higher statistical moments. The volume weighted average price (VWAP) is the simplest example of such a dependence. We derive the dependen... | https://arxiv.org/abs/2205.07256 | Academic Papers | svg |
65a42335e2d8c8e17aebfc3c902e91af8871e031bd8dd88f6d3aabd794873c1c | 2026-01-21T00:00:00-05:00 | Policy Learning under Endogeneity Using Instrumental Variables | arXiv:2206.09883v4 Announce Type: replace Abstract: I propose a framework for learning individualized policy rules in observational data settings characterized by endogenous treatment selection and the availability of an instrumental variable. I introduce encouragement rules that manipulate the instrument. By incorpora... | https://arxiv.org/abs/2206.09883 | Academic Papers | svg |
08e80a83f55606365b90e97601967bdecf55d5fc6668263b8498efb789fd7319 | 2026-01-21T00:00:00-05:00 | Identification in Multiple Treatment Models under Discrete Variation | arXiv:2307.06174v2 Announce Type: replace Abstract: We develop a marginal treatment effect based method to learn about causal effects in multiple treatment models with discrete instruments. We allow selection into treatment to be governed by a general class of threshold crossing models that permit multidimensional unob... | https://arxiv.org/abs/2307.06174 | Academic Papers | svg |
83252af480ac43fdcb6b761f12d8a5c83df32f5e2a5862deb137067d43f24d0e | 2026-01-21T00:00:00-05:00 | Interpreting Event-Studies from Recent Difference-in-Differences Methods | arXiv:2401.12309v2 Announce Type: replace Abstract: This note discusses the interpretation of event-study plots produced by recent difference-in-differences methods. I show that even when specialized to the case of non-staggered treatment timing, the default plots produced by software for several of the most popular re... | https://arxiv.org/abs/2401.12309 | Academic Papers | svg |
9082afbcfffba3d3f1ff830d1c9e290f7e4936e27b67403406962c11eafe1011 | 2026-01-21T00:00:00-05:00 | Database for the meta-analysis of the social cost of carbon (v2026.1) | arXiv:2402.09125v4 Announce Type: replace Abstract: A new version of the database for the meta-analysis of estimates of the social cost of carbon is presented. New records were added, and new fields on gender and stochasticity. | https://arxiv.org/abs/2402.09125 | Academic Papers | svg |
06bb4ecfae7819a4072489f5edaf998aff9a2c249bd4b5fe727a99b63664bf2b | 2026-01-21T00:00:00-05:00 | To be or not to be: Roughness or long memory in volatility? | arXiv:2403.12653v2 Announce Type: replace Abstract: We develop a framework for composite likelihood estimation of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that have be... | https://arxiv.org/abs/2403.12653 | Academic Papers | svg |
b95c797d23480a9e986b6b804f66fccd59d9c446acea32e3127741333e02558a | 2026-01-21T00:00:00-05:00 | Potential weights and implicit causal designs in linear regression | arXiv:2407.21119v4 Announce Type: replace Abstract: When we interpret linear regression as estimating causal effects justified by quasi-experimental treatment variation, what do we mean? This paper formalizes a minimal criterion for quasi-experimental interpretation and characterizes its necessary implications. A minim... | https://arxiv.org/abs/2407.21119 | Academic Papers | svg |
db0286452bb8a6dbc1da9c2b8a11362670f9d24b85a2c610fb5bfbd9176e273d | 2026-01-21T00:00:00-05:00 | The Turing Valley: How AI Capabilities Shape Labor Income | arXiv:2408.16443v2 Announce Type: replace Abstract: Current AI systems are better than humans in some knowledge dimensions but weaker in others. Guided by the long-standing vision of machine intelligence inspired by the Turing Test, AI developers increasingly seek to eliminate this "jagged" nature by pursuing Artificia... | https://arxiv.org/abs/2408.16443 | Academic Papers | svg |
f52ef9f3fae29ee497fe6d753ac18aacc4f78406fd41d13914ff9b3cafe7593d | 2026-01-21T00:00:00-05:00 | Uncertain and Asymmetric Forecasts | arXiv:2411.05938v2 Announce Type: replace Abstract: This paper develops distribution-based measures that extract policy-relevant information from subjective probability distributions beyond point forecasts. We introduce two complementary indicators that operationalize the second and third moments of beliefs. First, a N... | https://arxiv.org/abs/2411.05938 | Academic Papers | svg |
cf7870d77b0b98a6efcc0e1b6d73789413052b05321887f7f8b6aa1d86cb529b | 2026-01-21T00:00:00-05:00 | Sectorial Exclusion Criteria in the Marxist Analysis of the Average Rate of Profit: The United States Case (1960-2020) | arXiv:2501.06270v2 Announce Type: replace Abstract: The long term estimation of the Marxist average rate of profit does not adhere to a theoretically grounded standard regarding which economic activities should or should not be included for such purposes, which is relevant because methodological non uniformity can be a... | https://arxiv.org/abs/2501.06270 | Academic Papers | svg |
fc8e4676eddf546b11fde4c6128b377f9e25ff7c3170812e788e4c565587b4f7 | 2026-01-21T00:00:00-05:00 | Kotlarski's lemma for dyadic models | arXiv:2502.02734v2 Announce Type: replace Abstract: We show how to identify the distributions of the latent components in the two-way dyadic model for bipartite networks $y_{i,\ell}= \alpha_i+\eta_{\ell}+\varepsilon_{i,\ell}$. This is achieved by a repeated application of the extension of the classical lemma of Kotlars... | https://arxiv.org/abs/2502.02734 | Academic Papers | svg |
4f6e2afa8b2fceb779c37b838fa0f6f2a43f72f24d023a12a0ba0588b3f4a040 | 2026-01-21T00:00:00-05:00 | Trade and pollution: Evidence from India | arXiv:2502.09289v3 Announce Type: replace Abstract: What happens to pollution when developing countries open their borders to trade? Theoretical predictions are ambiguous, and empirical evidence remains limited. We study the effects of the 1991 Indian trade liberalization reform on water pollution. The reform abruptly ... | https://arxiv.org/abs/2502.09289 | Academic Papers | svg |
859964f6b7695b14cfe5a9056ed1356684ee977a6d4cc1a815c8bfc9a9764082 | 2026-01-21T00:00:00-05:00 | Policy Learning with Confidence | arXiv:2502.10653v3 Announce Type: replace Abstract: This paper introduces a rule for policy selection in the presence of estimation uncertainty, explicitly accounting for estimation risk. The rule belongs to the class of risk-aware rules on the efficient decision frontier, characterized as policies offering maximal est... | https://arxiv.org/abs/2502.10653 | Academic Papers | svg |
ddba326b581b2a83e2b06d9d4bf679f2858ad1765821abea08b3240d12bc44e5 | 2026-01-21T00:00:00-05:00 | Do Determinants of EV Purchase Intent vary across the Spectrum? Evidence from Bayesian Analysis of US Survey Data | arXiv:2504.09854v3 Announce Type: replace Abstract: While electric vehicle (EV) adoption has been widely studied, most research focuses on the average effects of predictors on purchase intent, overlooking variation across the distribution of EV purchase intent. This paper makes a threefold contribution by analyzing fou... | https://arxiv.org/abs/2504.09854 | Academic Papers | svg |
8c3e92c788f20b8bd3d1c65da92e74281b61afb4cf15e17d45adfb2967fa0fc2 | 2026-01-21T00:00:00-05:00 | Probabilistic Forecasting of Climate Policy Uncertainty: The Role of Macro-financial Variables and Google Search Data | arXiv:2507.12276v3 Announce Type: replace Abstract: Accurately forecasting Climate Policy Uncertainty (CPU) is essential for designing climate strategies that balance economic growth with environmental objectives. Elevated CPU levels can delay regulatory implementation, hinder investment in green technologies, and ampl... | https://arxiv.org/abs/2507.12276 | Academic Papers | svg |
1c4445bb8b9c2a473613e0a6caf42792fbe76287bc560b052ed1b2889e092266 | 2026-01-21T00:00:00-05:00 | From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles | arXiv:2512.13642v2 Announce Type: replace Abstract: Model combination is a powerful approach for achieving superior performance compared to selecting a single model. We study both theoretically and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks (MFESNs), which have been shown to achie... | https://arxiv.org/abs/2512.13642 | Academic Papers | svg |
ce20a52dfd707c9a383048ce169c65c836ca2a970a3595e84750d22fe106ab26 | 2026-01-21T00:00:00-05:00 | The Connection Between Monetary Policy and Housing Prices: Public Perception and Expert Communication | arXiv:2601.08957v2 Announce Type: replace Abstract: We study how the general public perceives the link between monetary policy and housing markets. Using a large-scale, cross-country survey experiment in Austria, Germany, Italy, Sweden, and the United Kingdom, we examine households' understanding of monetary policy, th... | https://arxiv.org/abs/2601.08957 | Academic Papers | svg |
bfbf84be5e1f96a5ea6636f03414222f4327d4f2314dc9af66ed11119b653c92 | 2026-01-21T00:00:00-05:00 | Assessing Utility of Differential Privacy for RCTs | arXiv:2309.14581v2 Announce Type: replace-cross Abstract: Randomized controlled trials (RCTs) have become powerful tools for assessing the impact of interventions and policies in many contexts. They are considered the gold standard for causal inference in the biomedical fields and many social sciences. Researchers have... | https://arxiv.org/abs/2309.14581 | Academic Papers | svg |
183ab9301e876bca62683e2c7fd08c89b60f2eedb970f633b77026d6a229e0d7 | 2026-01-21T00:00:00-05:00 | In Defense of the Pre-Test: Valid Inference when Testing Violations of Parallel Trends for Difference-in-Differences | arXiv:2510.26470v2 Announce Type: replace-cross Abstract: The difference-in-differences (DID) research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption. While the parallel trends assumption is counterfactual and cannot be tested directly, r... | https://arxiv.org/abs/2510.26470 | Academic Papers | svg |
1cea693283adb9dd2f355108a410b195effc5f63b559674323dbd4d366c6e7be | 2026-01-21T00:00:00-05:00 | Identifying Conditions Favouring Multiplicative Heterogeneity Models in Network Meta-Analysis | arXiv:2601.11735v1 Announce Type: new Abstract: Explicit modelling of between-study heterogeneity is essential in network meta-analysis (NMA) to ensure valid inference and avoid overstating precision. While the additive random-effects (RE) model is the conventional approach, the multiplicative-effect (ME) model remains... | https://arxiv.org/abs/2601.11735 | Academic Papers | svg |
db5988b9e971e8424914407fb92e685852fd54f5bdf083fba8b4debe80fe692f | 2026-01-21T00:00:00-05:00 | Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis | arXiv:2601.11790v1 Announce Type: new Abstract: Global sensitivity analysis of complex numerical simulators is often limited by the small number of model evaluations that can be afforded. In such settings, surrogate models built from a limited set of simulations can substantially reduce the computational burden, provid... | https://arxiv.org/abs/2601.11790 | Academic Papers | svg |
c7faa2dc61319a2bb86e32829ab648249639a69b5ad2dc85c140a054d1d2b3c4 | 2026-01-21T00:00:00-05:00 | Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI | arXiv:2601.11860v1 Announce Type: new Abstract: Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10), and systemic shocks like the COVID-19 pandemic. Addressing this ``aging'' effect via frequ... | https://arxiv.org/abs/2601.11860 | Academic Papers | svg |
16451dd384cd1eb2a4c263ec6e886044e8e5d71059ffb0a8a10ce8ba6b9e4d56 | 2026-01-21T00:00:00-05:00 | A Deep Learning-Copula Framework for Climate-Related Home Insurance Risk | arXiv:2601.11949v1 Announce Type: new Abstract: Extreme weather events are becoming more common, with severe storms, floods, and prolonged precipitation affecting communities worldwide. These shifts in climate patterns pose a direct threat to the insurance industry, which faces growing exposure to weather-related damag... | https://arxiv.org/abs/2601.11949 | Academic Papers | svg |
8b50264521b5a37650ec59b6c49651a71c5b3af42a9f034d1a688493cba3b8b1 | 2026-01-21T00:00:00-05:00 | A Kernel Approach for Semi-implicit Variational Inference | arXiv:2601.12023v1 Announce Type: new Abstract: Semi-implicit variational inference (SIVI) enhances the expressiveness of variational families through hierarchical semi-implicit distributions, but the intractability of their densities makes standard ELBO-based optimization biased. Recent score-matching approaches to SI... | https://arxiv.org/abs/2601.12023 | Academic Papers | svg |
30db2f4c5ee690f097e9758871598f48d8d0077ea0ff81731c4c983fd242b6a5 | 2026-01-21T00:00:00-05:00 | Estimations of Extreme CoVaR and CoES under Asymptotic Independence | arXiv:2601.12031v1 Announce Type: new Abstract: The two popular systemic risk measures CoVaR (Conditional Value-at-Risk) and CoES (Conditional Expected Shortfall) have recently been receiving growing attention on applications in economics and finance. In this paper, we study the estimations of extreme CoVaR and CoES wh... | https://arxiv.org/abs/2601.12031 | Academic Papers | svg |
b1553a5f4d0412f2b16fa30c2353e446ee8826acc50a7d564203b4acde50533c | 2026-01-21T00:00:00-05:00 | Lost in Aggregation: The Causal Interpretation of the IV Estimand | arXiv:2601.12120v1 Announce Type: new Abstract: Instrumental variable based estimation of a causal effect has emerged as a standard approach to mitigate confounding bias in the social sciences and epidemiology, where conducting randomized experiments can be too costly or impossible. However, justifying the validity of ... | https://arxiv.org/abs/2601.12120 | Academic Papers | svg |
b429dbf103bbbfe36c39c85bd6ed2e22b69dab8d0320249be8ad89123eed81c5 | 2026-01-21T00:00:00-05:00 | Using Directed Acyclic Graphs to Illustrate Common Biases in Diagnostic Test Accuracy Studies | arXiv:2601.12167v1 Announce Type: new Abstract: Background: Diagnostic test accuracy (DTA) studies, like etiological studies, are susceptible to various biases including reference standard error bias, partial verification bias, spectrum effect, confounding, and bias from misassumption of conditional independence. While... | https://arxiv.org/abs/2601.12167 | Academic Papers | svg |
15a679f154f9da7101402caa71226cde413918ce9c02eba19ce994bc4a32ebf8 | 2026-01-21T00:00:00-05:00 | A warping function-based control chart for detecting distributional changes in damage-sensitive features for structural condition assessment | arXiv:2601.12221v1 Announce Type: new Abstract: Data-driven damage detection methods achieve damage identification by analyzing changes in damage-sensitive features (DSFs) derived from structural health monitoring (SHM) data. The core reason for their effectiveness lies in the fact that damage or structural state trans... | https://arxiv.org/abs/2601.12221 | Academic Papers | svg |
a9e5ae0b06d4784e20c0937ac29851a49409c48a92ab635b820696558ba9c79a | 2026-01-21T00:00:00-05:00 | A Machine Learning--Based Surrogate EKMA Framework for Diagnosing Urban Ozone Formation Regimes: Evidence from Los Angeles | arXiv:2601.12321v1 Announce Type: new Abstract: Surface ozone pollution remains a persistent challenge in many metropolitan regions worldwide, as the nonlinear dependence of ozone formation on nitrogen oxides and volatile organic compounds (VOCs) complicates the design of effective emission control strategies. While ch... | https://arxiv.org/abs/2601.12321 | Academic Papers | svg |
c14ba6a21623d5b66f7e8fb6efd55b6bd80872fd4e8f16529b1de3e229b63e4f | 2026-01-21T00:00:00-05:00 | Single-index Semiparametric Transformation Cure Models with Interval-censored Data | arXiv:2601.12370v1 Announce Type: new Abstract: Interval censored data commonly arise in medical studies when the event time of interest is only known to lie within an interval. In the presence of a cure subgroup, conventional mixture cure models typically assume a logistic model for the uncure probability and a propor... | https://arxiv.org/abs/2601.12370 | Academic Papers | svg |
666c3a9496302ec6a6c86144d94a71bf05af34d5b074fe918720bca73fc9eea0 | 2026-01-21T00:00:00-05:00 | Robust semi-parametric mixtures of linear experts using the contaminated Gaussian distribution | arXiv:2601.12425v1 Announce Type: new Abstract: Semi- and non-parametric mixture of regressions are a very useful flexible class of mixture of regressions in which some or all of the parameters are non-parametric functions of the covariates. These models are, however, based on the Gaussian assumption of the component e... | https://arxiv.org/abs/2601.12425 | Academic Papers | svg |
d800b9d775a2e0c9661bfc6f960d20211b4fe4eae938406bc7aa5f46688ac516 | 2026-01-21T00:00:00-05:00 | Assessing Interactive Causes of an Occurred Outcome Due to Two Binary Exposures | arXiv:2601.12478v1 Announce Type: new Abstract: In contrast to evaluating treatment effects, causal attribution analysis focuses on identifying the key factors responsible for an observed outcome. For two binary exposure variables and a binary outcome variable, researchers need to assess not only the likelihood that an... | https://arxiv.org/abs/2601.12478 | Academic Papers | svg |
74f45605cd4a63ee51d406bf83f4a395590d81fe6ddf45aaa8c712296ae55030 | 2026-01-21T00:00:00-05:00 | Bayesian Inference for Partially Observed McKean-Vlasov SDEs with Full Distribution Dependence | arXiv:2601.12515v1 Announce Type: new Abstract: McKean-Vlasov stochastic differential equations (MVSDEs) describe systems whose dynamics depend on both individual states and the population distribution, and they arise widely in neuroscience, finance, and epidemiology. In many applications the system is only partially o... | https://arxiv.org/abs/2601.12515 | Academic Papers | svg |
46aaaa5c36673e6d77e0cb0ffb7013ac971f8ef1cf1b864318c594ebed4f68ee | 2026-01-21T00:00:00-05:00 | Stop using limiting stimuli as a measure of sensitivities of energetic materials | arXiv:2601.12552v1 Announce Type: new Abstract: Accurately estimating the sensitivity of explosive materials is a potentially life-saving task which requires standardised protocols across nations. One of the most widely applied procedures worldwide is the so-called '1-In-6' test from the United Nations (UN) Manual of T... | https://arxiv.org/abs/2601.12552 | Academic Papers | svg |
fa631033905be6d664b115c976a2567b847f248f23a52d0ff272a87aaaefbb34 | 2026-01-21T00:00:00-05:00 | A Theory of Diversity for Random Matrices with Applications to In-Context Learning of Schr\"odinger Equations | arXiv:2601.12587v1 Announce Type: new Abstract: We address the following question: given a collection $\{\mathbf{A}^{(1)}, \dots, \mathbf{A}^{(N)}\}$ of independent $d \times d$ random matrices drawn from a common distribution $\mathbb{P}$, what is the probability that the centralizer of $\{\mathbf{A}^{(1)}, \dots, \ma... | https://arxiv.org/abs/2601.12587 | Academic Papers | svg |
c867b29f11e3faf245fc6233a090d30f5b7ab250a8d1afc27be4392bfbe1cc00 | 2026-01-21T00:00:00-05:00 | Quasi-Bayesian Variable Selection: Model Selection without a Model | arXiv:2601.12767v1 Announce Type: new Abstract: Bayesian inference offers a powerful framework for variable selection by incorporating sparsity through prior beliefs and quantifying uncertainty about parameters, leading to consistent procedures with good finite-sample performance. However, accurately quantifying uncert... | https://arxiv.org/abs/2601.12767 | Academic Papers | svg |
ef7e25b83e31a22ea3b4e9353792534215c58bc623704e12659ecc14f1aa1a64 | 2026-01-21T00:00:00-05:00 | The impact of abnormal temperatures on crop yields in Italy: a functional quantile regression approach | arXiv:2601.12864v1 Announce Type: new Abstract: In this study, we apply functional regression analysis to identify the specific within-season periods during which temperature and precipitation anomalies most affect crop yields. Using provincial data for Italy from 1952 to 2023, we analyze two major cereals, maize and s... | https://arxiv.org/abs/2601.12864 | Academic Papers | svg |
93aa85d7bcd16fdebeb5fc375a4a65436f78345162a97d9a4ea894beb61699d4 | 2026-01-21T00:00:00-05:00 | Guidance for Addressing Individual Time Effects in Cohort Stepped Wedge Cluster Randomized Trials: A Simulation Study | arXiv:2601.12930v1 Announce Type: new Abstract: Background: Stepped wedge cluster randomized trials (SW-CRTs) involve sequential measurements within clusters over time. Initially, all clusters start in the control condition before crossing over to the intervention on a staggered schedule. In cohort designs, secular tre... | https://arxiv.org/abs/2601.12930 | Academic Papers | svg |
e44cff2e674299fd9ff04284ed009586afa205282da842a07f2e7665f2aba0b5 | 2026-01-21T00:00:00-05:00 | Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores | arXiv:2601.13150v1 Announce Type: new Abstract: Design-based inference, also known as randomization-based or finite-population inference, provides a principled framework for causal and descriptive analyses that attribute randomness solely to the design mechanism (e.g., treatment assignment, sampling, or missingness) wi... | https://arxiv.org/abs/2601.13150 | Academic Papers | svg |
2631c898df73df9dd80095893ce894424b56de1c52b6f578fd1ed65ad1bacc45 | 2026-01-21T00:00:00-05:00 | Empirical Risk Minimization with $f$-Divergence Regularization | arXiv:2601.13191v1 Announce Type: new Abstract: In this paper, the solution to the empirical risk minimization problem with $f$-divergence regularization (ERM-$f$DR) is presented and conditions under which the solution also serves as the solution to the minimization of the expected empirical risk subject to an $f$-dive... | https://arxiv.org/abs/2601.13191 | Academic Papers | svg |
1a27bcb06e0e315c587ad9886a2ae8ed7e5624bc1508400ec7bb91de1af6c00f | 2026-01-21T00:00:00-05:00 | Improving Geopolitical Forecasts with Bayesian Networks | arXiv:2601.13362v1 Announce Type: new Abstract: This study explores how Bayesian networks (BNs) can improve forecast accuracy compared to logistic regression and recalibration and aggregation methods, using data from the Good Judgment Project. Regularized logistic regression models and a baseline recalibrated aggregate... | https://arxiv.org/abs/2601.13362 | Academic Papers | svg |
878b5a01eb334390229d000de65ef0d26ece6d8baee43c2a5263bbd1912af2fe | 2026-01-21T00:00:00-05:00 | A Two-Stage Bayesian Framework for Multi-Fidelity Online Updating of Spatial Fragility Fields | arXiv:2601.13396v1 Announce Type: new Abstract: This paper addresses a long-standing gap in natural hazard modeling by unifying physics-based fragility functions with real-time post-disaster observations. It introduces a Bayesian framework that continuously refines regional vulnerability estimates as new data emerges. ... | https://arxiv.org/abs/2601.13396 | Academic Papers | svg |
9caee830295ff74936ca1241a805fbfc8389d93035319a46a9ca4742ff1f948b | 2026-01-21T00:00:00-05:00 | Associating High-Dimensional Longitudinal Datasets through an Efficient Cross-Covariance Decomposition | arXiv:2601.13405v1 Announce Type: new Abstract: Understanding associations between paired high-dimensional longitudinal datasets is a fundamental yet challenging problem that arises across scientific domains, including longitudinal multi-omic studies. The difficulty stems from the complex, time-varying cross-covariance... | https://arxiv.org/abs/2601.13405 | Academic Papers | svg |
20ce18ee5dc6981d09cc902cb08ef0fce681c522238836ca4b2808e7b0ac4d80 | 2026-01-21T00:00:00-05:00 | Pathway-based Bayesian factor models for gene expression data | arXiv:2601.13419v1 Announce Type: new Abstract: Interpreting gene expression data requires methods that can uncover coordinated patterns corresponding to biological pathways. Traditional approaches such as principal component analysis and factor models reduce dimensionality, but latent components may have unclear biolo... | https://arxiv.org/abs/2601.13419 | Academic Papers | svg |
f56b3d43ea6d37fcceef4f583bbc504de747659bca56c00fd62a9db711e068fb | 2026-01-21T00:00:00-05:00 | Identifying Causes of Test Unfairness: Manipulability and Separability | arXiv:2601.13449v1 Announce Type: new Abstract: Differential item functioning (DIF) is a widely used statistical notion for identifying items that may disadvantage specific groups of test-takers. These groups are often defined by non-manipulable characteristics, e.g., gender, race/ethnicity, or English-language learner... | https://arxiv.org/abs/2601.13449 | Academic Papers | svg |
3b6c741cc75aa36f64eab37d68309a33657d5f4258a02ad2a1674564374929bc | 2026-01-21T00:00:00-05:00 | Categorical distance correlation under general encodings and its application to high-dimensional feature screening | arXiv:2601.13454v1 Announce Type: new Abstract: In this paper, we extend distance correlation to categorical data with general encodings, such as one-hot encoding for nominal variables and semicircle encoding for ordinal variables. Unlike existing methods, our approach leverages the spacing information between categori... | https://arxiv.org/abs/2601.13454 | Academic Papers | svg |
519d8963e5acdcf9a6699d8ad2a1f9d76542e0f284ad524a3a094bfc0b79af60 | 2026-01-21T00:00:00-05:00 | Two-stage least squares with clustered data | arXiv:2601.13507v1 Announce Type: new Abstract: Clustered data -- where units of observation are nested within higher-level groups, such as repeated measurements on users, or panel data of firms, industries, or geographic regions -- are ubiquitous in business research. When the objective is to estimate the causal effec... | https://arxiv.org/abs/2601.13507 | Academic Papers | svg |
a913e5974297731393e57adaa96e814da363d52b001435fb47265c3a57bcaba7 | 2026-01-21T00:00:00-05:00 | Post-selection inference for penalized M-estimators via score thinning | arXiv:2601.13514v1 Announce Type: new Abstract: We consider inference for M-estimators after model selection using a sparsity-inducing penalty. While existing methods for this task require bespoke inference procedures, we propose a simpler approach, which relies on two insights: (i) adding and subtracting carefully-con... | https://arxiv.org/abs/2601.13514 | Academic Papers | svg |
e6cc0d1db381cf776404edaa3f470efc82b480c76b2751a39534c1b47efe16c8 | 2026-01-21T00:00:00-05:00 | What is Overlap Weighting, How Has it Evolved, and When to Use It for Causal Inference? | arXiv:2601.13535v1 Announce Type: new Abstract: The growing availability of large health databases has expanded the use of observational studies for comparative effectiveness research. Unlike randomized trials, observational studies must adjust for systematic differences in patient characteristics between treatment gro... | https://arxiv.org/abs/2601.13535 | Academic Papers | svg |
c63b8c314a8d5b9ce37fbcc5dde4f5c2464b13762336180be99fc213a62bf265 | 2026-01-21T00:00:00-05:00 | Are Large Language Models able to Predict Highly Cited Papers? Evidence from Statistical Publications | arXiv:2601.13627v1 Announce Type: new Abstract: Predicting highly-cited papers is a long-standing challenge due to the complex interactions of research content, scholarly communities, and temporal dynamics. Recent advances in large language models (LLMs) raise the question of whether early-stage textual information can... | https://arxiv.org/abs/2601.13627 | Academic Papers | svg |
7bec013783d0f4d90e4505c4ccb96cda209c4efe13e9da2a129f05446e0aa40e | 2026-01-21T00:00:00-05:00 | Correction of Pooling Matrix Mis-specifications in Compressed Sensing Based Group Testing | arXiv:2601.13641v1 Announce Type: new Abstract: Compressed sensing, which involves the reconstruction of sparse signals from an under-determined linear system, has been recently used to solve problems in group testing. In a public health context, group testing aims to determine the health status values of p subjects fr... | https://arxiv.org/abs/2601.13641 | Academic Papers | svg |
b5bc07a14ebe053942ed539f7b2885494e2c6e61c49a4c6f0ede7aa319a84ea6 | 2026-01-21T00:00:00-05:00 | Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning | arXiv:2601.13642v1 Announce Type: new Abstract: Average-reward reinforcement learning offers a principled framework for long-term decision-making by maximizing the mean reward per time step. Although Q-learning is a widely used model-free algorithm with established sample complexity in discounted and finite-horizon Mar... | https://arxiv.org/abs/2601.13642 | Academic Papers | svg |
474ab32c8b89703b4633ffc360bbd505ccf97222b3ca4fb02143e7049ff7e77d | 2026-01-21T00:00:00-05:00 | Building a Standardised Statistical Reporting Toolbox in an Academic Oncology Clinical Trials Unit: The grstat R Package | arXiv:2601.13755v1 Announce Type: new Abstract: Academic Clinical Trial Units frequently face fragmented statistical workflows, leading to duplicated effort, limited collaboration, and inconsistent analytical practices. To address these challenges within an oncology Clinical Trial Unit, we developed grstat, an R packag... | https://arxiv.org/abs/2601.13755 | Academic Papers | svg |
23e918e2772167e8c07e5c6cae99db29ad14666ae525f19d90cb8b4594b74b91 | 2026-01-21T00:00:00-05:00 | ChauBoxplot and AdaptiveBoxplot: two R packages for boxplot-based outlier detection | arXiv:2601.13759v1 Announce Type: new Abstract: Tukey's boxplot is widely used for outlier detection; however, its classic fixed-fence rule tends to flag an excessive number of outliers as the sample size grows. To address this limitation, we introduce two new R packages, ChauBoxplot and AdaptiveBoxplot, which implemen... | https://arxiv.org/abs/2601.13759 | Academic Papers | svg |
5a3ec8fe95659f6d773d837a1b05a9e281e459d67b7f63e35193384e4e64fc66 | 2026-01-21T00:00:00-05:00 | An Adaptive Phase II Trial Design for Dose Selection and Addition in Microfilarial Infections | arXiv:2601.13784v1 Announce Type: new Abstract: We propose a frequentist adaptive phase 2 trial design to evaluate the safety and efficacy of three treatment regimens (doses) compared to placebo for four types of helminth (worm) infections. This trial will be carried out in four Subsaharan African countries from spring... | https://arxiv.org/abs/2601.13784 | Academic Papers | svg |
23bef067c53951aaadd4dbe564ad0337b596184551a27ac0580582cd8804c254 | 2026-01-21T00:00:00-05:00 | Unified Unbiased Variance Estimation for MMD: Robust Finite-Sample Performance with Imbalanced Data and Exact Acceleration under Null and Alternative Hypotheses | arXiv:2601.13874v1 Announce Type: new Abstract: The maximum mean discrepancy (MMD) is a kernel-based nonparametric statistic for two-sample testing, whose inferential accuracy depends critically on variance characterization. Existing work provides various finite-sample estimators of the MMD variance, often differing un... | https://arxiv.org/abs/2601.13874 | Academic Papers | svg |
0064a644f863cab8d984cee9ffa3cfb1ca8422df11d62bec35635a4ffd874c99 | 2026-01-21T00:00:00-05:00 | Modeling Zero-Inflated Longitudinal Circular Data Using Bayesian Methods: Application to Ophthalmology | arXiv:2601.13998v1 Announce Type: new Abstract: This paper introduces the modeling of circular data with excess zeros under a longitudinal framework, where the response is a circular variable and the covariates can be both linear and circular in nature. In the literature, various circular-circular and circular-linear r... | https://arxiv.org/abs/2601.13998 | Academic Papers | svg |
956e237c7f20ec6702f4c991cd1b6d858c791c59d1c0d574750ed8bd295429dd | 2026-01-21T00:00:00-05:00 | Intermittent time series forecasting: local vs global models | arXiv:2601.14031v1 Announce Type: new Abstract: Intermittent time series, characterised by the presence of a significant amount of zeros, constitute a large percentage of inventory items in supply chain. Probabilistic forecasts are needed to plan the inventory levels; the predictive distribution should cover non-negati... | https://arxiv.org/abs/2601.14031 | Academic Papers | svg |
62a50d63f88639b37711a6971f6cccbb05caee546dfc5f8e257b7dc2d28cb637 | 2026-01-21T00:00:00-05:00 | Tail-Aware Density Forecasting of Locally Explosive Time Series: A Neural Network Approach | arXiv:2601.14049v1 Announce Type: new Abstract: This paper proposes a Mixture Density Network for forecasting time series that exhibit locally explosive behavior. By incorporating skewed t-distributions as mixture components, our approach offers enhanced flexibility in capturing the skewed, heavy-tailed, and potentiall... | https://arxiv.org/abs/2601.14049 | Academic Papers | svg |
5b884e7ad0caa3a96439967d4752949b76191a79b0d53349d21e46b1382ac342 | 2026-01-21T00:00:00-05:00 | Factor Analysis of Multivariate Stochastic Volatility Model | arXiv:2601.14199v1 Announce Type: new Abstract: Modeling the time-varying covariance structures of high-dimensional variables is critical across diverse scientific and industrial applications; however, existing approaches exhibit notable limitations in either modeling flexibility or inferential efficiency. For instance... | https://arxiv.org/abs/2601.14199 | Academic Papers | svg |
47401838181d4aae8336076fd5ab1e1bddfe3f69913703e5e26a1ab2ca04e0bf | 2026-01-21T00:00:00-05:00 | Verifying Physics-Informed Neural Network Fidelity using Classical Fisher Information from Differentiable Dynamical System | arXiv:2601.11638v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differential equations and modeling physical systems by embedding physical laws into the learning process. However, rigorously quantifying how well a PINN captures the complete dynamica... | https://arxiv.org/abs/2601.11638 | Academic Papers | svg |
04c8c659f4df3dabda329d7b7521498e7d91c5af2dc79a4bbbb2723bd78529c0 | 2026-01-21T00:00:00-05:00 | Task-tailored Pre-processing: Fair Downstream Supervised Learning | arXiv:2601.11897v1 Announce Type: cross Abstract: Fairness-aware machine learning has recently attracted various communities to mitigate discrimination against certain societal groups in data-driven tasks. For fair supervised learning, particularly in pre-processing, there have been two main categories: data fairness a... | https://arxiv.org/abs/2601.11897 | Academic Papers | svg |
76eb72898f563890335793f140e65d17f591759eb98f9b06624117d94a533598 | 2026-01-21T00:00:00-05:00 | Privacy-Preserving Cohort Analytics for Personalized Health Platforms: A Differentially Private Framework with Stochastic Risk Modeling | arXiv:2601.12105v1 Announce Type: cross Abstract: Personalized health analytics increasingly rely on population benchmarks to provide contextual insights such as ''How do I compare to others like me?'' However, cohort-based aggregation of health data introduces nontrivial privacy risks, particularly in interactive and ... | https://arxiv.org/abs/2601.12105 | Academic Papers | svg |
949522e06fb65a66cb5b28bb121d34b02d09bc0e1913d17e49d83936562e775b | 2026-01-21T00:00:00-05:00 | Distributional Fitting and Tail Analysis of Lead-Time Compositions: Nights vs. Revenue on Airbnb | arXiv:2601.12175v1 Announce Type: cross Abstract: We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume) and Gross Booking Value (revenue), treating each day's allocation across 0-365 days as a compositional vector. The data span 2,557 days from January 2019 through December 2025... | https://arxiv.org/abs/2601.12175 | Academic Papers | svg |
47340e0e067d3a10f005c28078d6a94c7f166c4dbb8235bb0218754f9065b6d3 | 2026-01-21T00:00:00-05:00 | Extracting useful information about reversible evolutionary processes from irreversible evolutionary accumulation models | arXiv:2601.13010v1 Announce Type: cross Abstract: Evolutionary accumulation models (EvAMs) are an emerging class of machine learning methods designed to infer the evolutionary pathways by which features are acquired. Applications include cancer evolution (accumulation of mutations), anti-microbial resistance (accumulat... | https://arxiv.org/abs/2601.13010 | Academic Papers | svg |
c2c5b0be024f0b28fb38efb0cf16b503c88254da62d2299c2277f8ca477ce65c | 2026-01-21T00:00:00-05:00 | Optimal Calibration of the endpoint-corrected Hilbert Transform | arXiv:2601.13962v1 Announce Type: cross Abstract: Accurate, low-latency estimates of the instantaneous phase of oscillations are essential for closed-loop sensing and actuation, including (but not limited to) phase-locked neurostimulation and other real-time applications. The endpoint-corrected Hilbert transform (ecHT)... | https://arxiv.org/abs/2601.13962 | Academic Papers | svg |
72f6f4a88b10ff26568e69359a113ada579ada5641727a6234712d71487a4d54 | 2026-01-21T00:00:00-05:00 | Demystifying the trend of the healthcare index: Is historical price a key driver? | arXiv:2601.14062v1 Announce Type: cross Abstract: Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, d... | https://arxiv.org/abs/2601.14062 | Academic Papers | svg |
aa7123676ea7a5f52ec3932add65afc0fbb5bf5f2c6e82300fdbf442ac8add4c | 2026-01-21T00:00:00-05:00 | Penalizing Localized Dirichlet Energies in Low Rank Tensor Products | arXiv:2601.14173v1 Announce Type: cross Abstract: We study low-rank tensor-product B-spline (TPBS) models for regression tasks and investigate Dirichlet energy as a measure of smoothness. We show that TPBS models admit a closed-form expression for the Dirichlet energy, and reveal scenarios where perfect interpolation i... | https://arxiv.org/abs/2601.14173 | Academic Papers | svg |
ddbf8a3ecf6c112c0638fbcf27684f6b7988374cb4d8e56cae8d58ae846daed9 | 2026-01-21T00:00:00-05:00 | Q-learning with Adjoint Matching | arXiv:2601.14234v1 Announce Type: cross Abstract: We propose Q-learning with Adjoint Matching (QAM), a novel TD-based reinforcement learning (RL) algorithm that tackles a long-standing challenge in continuous-action RL: efficient optimization of an expressive diffusion or flow-matching policy with respect to a paramete... | https://arxiv.org/abs/2601.14234 | Academic Papers | svg |
99fa9abe7d47ffe9377aea62be0471565ec84a53551577823fb1c465530121b9 | 2026-01-21T00:00:00-05:00 | Non-parametric Bayesian inference via loss functions under model misspecification | arXiv:2103.04086v5 Announce Type: replace Abstract: In the usual Bayesian setting, a full probabilistic model is required to link the data and parameters, and the form of this model and the inference and prediction mechanisms are specified via de Finetti's representation. In general, such a formulation is not robust to... | https://arxiv.org/abs/2103.04086 | Academic Papers | svg |
3e887c3171303916906e69067f39a01bc39cb1718eaa6548755a12cc775c07ff | 2026-01-21T00:00:00-05:00 | Bayesian Evidence Synthesis for the common effect model | arXiv:2103.13236v2 Announce Type: replace Abstract: Bayes Factors, the Bayesian tool for hypothesis testing, are receiving increasing attention in the literature. Compared to their frequentist rivals ($p$-values or test statistics), Bayes Factors have the conceptual advantage of providing evidence both for and against ... | https://arxiv.org/abs/2103.13236 | Academic Papers | svg |
28bf13a5b65572458e617a3a1edd3290b4a426e3942aff6c05c8f99693a40f30 | 2026-01-21T00:00:00-05:00 | Classification of high-dimensional data with spiked covariance matrix structure | arXiv:2110.01950v3 Announce Type: replace Abstract: We study the classification problem for high-dimensional data with $n$ observations on $p$ features where the $p \times p$ covariance matrix $\Sigma$ exhibits a spiked eigenvalue structure and the vector $\zeta$, given by the difference between the {\em whitened} mean... | https://arxiv.org/abs/2110.01950 | Academic Papers | svg |
2fb462cca1e8e65ed91eecbe8ce1133a53f8688dd256e990e1e7f54ceda95979 | 2026-01-21T00:00:00-05:00 | Transformed Linear Prediction for Extremes | arXiv:2111.03754v5 Announce Type: replace Abstract: We address the problem of prediction for extreme observations by proposing an extremal linear prediction method. We construct an inner product space of nonnegative random variables derived from transformed-linear combinations of independent regularly varying random va... | https://arxiv.org/abs/2111.03754 | Academic Papers | svg |
83590f11f58e4065cb73aaecdfe10a3e189f724ee0d5e4555fdc65136e09405f | 2026-01-21T00:00:00-05:00 | dynamite: An R Package for Dynamic Multivariate Panel Models | arXiv:2302.01607v4 Announce Type: replace Abstract: dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising multiple measurements per multiple individuals measured in time. The package supports joint modeling of multiple response variables, time-varying and time-invariant effect... | https://arxiv.org/abs/2302.01607 | Academic Papers | svg |
3799302d8faa5f6ca9fa258464b27faab6f5dca5cea828cb0f752c1ff4710f96 | 2026-01-21T00:00:00-05:00 | BESS: A Bayesian Estimator of Sample Size | arXiv:2404.07923v4 Announce Type: replace Abstract: We consider a Bayesian framework for estimating the sample size of a clinical trial. The new approach, called BESS, is built upon three pillars: Sample size of the trial, Evidence from the observed data, and Confidence of the final decision in the posterior inference.... | https://arxiv.org/abs/2404.07923 | Academic Papers | svg |
ddddd039a8bb4920a0b180754925c3bdcaf85531b6e6ede317b34399fdb18740 | 2026-01-21T00:00:00-05:00 | Asymmetry Analysis of Bilateral Shapes | arXiv:2407.17225v2 Announce Type: replace Abstract: Many biological objects possess bilateral symmetry about a midline or midplane, up to a ``noise'' term. This paper uses landmark-based methods to measure departures from bilateral symmetry, especially for the two-group problem where one group is more asymmetric than t... | https://arxiv.org/abs/2407.17225 | Academic Papers | svg |
a5c63fe8e15bf973341743080363e0a9fb51dca464d3097e59ed695cb4632b8b | 2026-01-21T00:00:00-05:00 | Robust Inference for Non-Linear Regression Models with Applications in Enzyme Kinetics | arXiv:2409.15995v2 Announce Type: replace Abstract: Despite linear regression being the most popular statistical modelling technique, in real-life we often need to deal with situations where the true relationship between the response and the covariates is nonlinear in parameters. In such cases one needs to adopt approp... | https://arxiv.org/abs/2409.15995 | Academic Papers | svg |
b73d421dca28b1b2df5ab9cb62a1f995d207e4af28af6b7f13c6b74178e70350 | 2026-01-21T00:00:00-05:00 | Experimentation on Endogenous Graphs | arXiv:2410.09267v2 Announce Type: replace Abstract: We study experimentation under endogenous network interference. Interference patterns are mediated by an endogenous graph, where edges can be formed or eliminated as a result of treatment. We show that conventional estimators are biased in these circumstances, and pre... | https://arxiv.org/abs/2410.09267 | Academic Papers | svg |
bc9ae116ac0b6a444c463c9749a1d8a04e2aa2b5034834112674028409b793c3 | 2026-01-21T00:00:00-05:00 | Dynamic networks clustering via mirror distance | arXiv:2412.19012v2 Announce Type: replace Abstract: The classification of different patterns of network evolution, for example in brain connectomes or social networks, is a key problem in network inference and modern data science. Building on the notion of a network's Euclidean mirror, which captures its evolution as a... | https://arxiv.org/abs/2412.19012 | Academic Papers | svg |
59dbe4f43d016d4b0a0f2ee9cdfebb377efdf4c10e88d03ec821786bd64dfab6 | 2026-01-21T00:00:00-05:00 | A Spatio-Temporal Dirichlet Process Mixture Model on Linear Networks for Crime Data | arXiv:2501.08673v2 Announce Type: replace Abstract: Analyzing crime events is crucial to understand crime dynamics and it is largely helpful for constructing prevention policies. Point processes specified on linear networks can provide a more accurate description of crime incidents by considering the geometry of the ci... | https://arxiv.org/abs/2501.08673 | Academic Papers | svg |
27eeaafc98e15c2b5b4ed2178ab3761ebae111bb80fec9def2292b695830527f | 2026-01-21T00:00:00-05:00 | A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications | arXiv:2501.17512v3 Announce Type: replace Abstract: As Federated Learning (FL) expands, the challenge of non-independent and identically distributed (non-IID) data becomes critical. Clustered Federated Learning (CFL) addresses this by training multiple specialized models, each representing a group of clients with simil... | https://arxiv.org/abs/2501.17512 | Academic Papers | svg |
cd14b947c71e1d81541fb68b18fcdbc5c733e1525d37e41256ab217f592a97f8 | 2026-01-21T00:00:00-05:00 | Network-Level Measures of Mobility from Aggregated Origin-Destination Data | arXiv:2502.04162v2 Announce Type: replace Abstract: We introduce a framework for defining and interpreting collective mobility measures from spatially and temporally aggregated origin--destination (OD) data. Rather than characterizing individual behavior, these measures describe properties of the mobility system itself... | https://arxiv.org/abs/2502.04162 | Academic Papers | svg |
26c70fa089d7bf1a6dc6f014106517b50eb473dc5105a568115a97940ccfbca3 | 2026-01-21T00:00:00-05:00 | Variable transformations in consistent loss functions | arXiv:2502.16542v3 Announce Type: replace Abstract: The empirical use of variable transformations within (strictly) consistent loss functions is widespread, yet a theoretical understanding is lacking. To address this gap, we develop a theoretical framework that establishes formal characterizations of (strict) consisten... | https://arxiv.org/abs/2502.16542 | Academic Papers | svg |
73d7a1a5cb2fa83260e224d4199c05cecc404f1d69e00f3c69579ad97233f81e | 2026-01-21T00:00:00-05:00 | Fairness-aware kidney exchange and kidney paired donation | arXiv:2503.06431v2 Announce Type: replace Abstract: The kidney paired donation (KPD) program provides an innovative solution to overcome incompatibility challenges in kidney transplants by matching incompatible donor-patient pairs and facilitating kidney exchanges. To address unequal access to transplant opportunities,... | https://arxiv.org/abs/2503.06431 | Academic Papers | svg |
7444d3139381c49a62debe1cf904b397f87eb4b5f9729f36e9758ee7b1c1c720 | 2026-01-21T00:00:00-05:00 | Applications of higher order Markov models and Pressure Index to strategize controlled run chases in Twenty20 cricket | arXiv:2505.01849v2 Announce Type: replace Abstract: In limited overs cricket, the team batting first posts a target score for the team batting second to achieve in order to win the match. The team batting second is constrained by decreasing resources in terms of number of balls left and number of wickets in hand in the... | https://arxiv.org/abs/2505.01849 | Academic Papers | svg |
1585030cf7f60a1c0d5ea218aa3cb175b4d00fd311bb8caddec0220163556f89 | 2026-01-21T00:00:00-05:00 | Flow-based Generative Modeling of Potential Outcomes and Counterfactuals | arXiv:2505.16051v3 Announce Type: replace Abstract: Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinical settings where treatment choices must be tailored to each patient rather than guided solely by population averages. We propo... | https://arxiv.org/abs/2505.16051 | Academic Papers | svg |
6fc60f6061143a9e05034ed5b51c89e0dc78473509f60116b090847894509c0b | 2026-01-21T00:00:00-05:00 | ALPCAHUS: Subspace Clustering for Heteroscedastic Data | arXiv:2505.18918v3 Announce Type: replace Abstract: Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend PCA to the union of subspace (UoS) setting for clustering data that comes from multiple subspaces like K-Subspaces (KSS). Howev... | https://arxiv.org/abs/2505.18918 | Academic Papers | svg |
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