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Third, we establish the consistency of our spectral estimator in the double-asymptotic regime where both the number of subjects N𝑁Nitalic_N and the number of items J𝐽Jitalic_J grow to infinity. Both the population parameters and the individual membership scores can be consistently estimated on average.
In the simulat... | **A**: Section 4 proposes a spectral estimation algorithm and establishes its consistency.
**B**: A real data example also illustrates that meaningful interpretation can be drawn after applying our proposed method.
The rest of the paper is structured as follows.
**C**: Section 2 introduces the model setup and lays ... | ACB | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> If the series terms and the error bound are rational, the algorithm can be implemented using only rational arithmetic.
Standard simulation methods, as implemented in computer software, typically define the parameter τ𝜏\tauitalic_τ as a floating-point value. This inevitably incur... | **A**: The algorithm requires a positive series representation of τ𝜏\tauitalic_τ, and a bound for the truncation error that converges to 00.
**B**: The method presented in this paper avoids that problem, and generates a random variable with the exact parameter τ𝜏\tauitalic_τ..
**C**:
An algorithm has been proposed... | CAB | CAB | CAB | ACB | Selection 3 |
The literature on the estimation of low-dimensional structures (or filaments) is rich, and different approaches use different geometric ideas. For example, the local principal curves (Einbeck et al., 2005, 2008) are formed by tracking the localized version of the first principal component directions, but the method re... | **A**: The medial axis of the data
distribution’s support (Genovese et al., 2012b) can also be used to estimate filaments, under the assumption that the noise around the filaments is symmetric.
**B**: (2006) can be used to detect the presence of a single filament in a noise background.
**C**: The candy model (Stoica ... | CAB | CAB | CAB | CAB | Selection 2 |
In Section 4, we also provide detailed descriptions of specific examples of algorithms. <|MaskedSetence|> The moving target MH algorithm is a specific example of this [95]. Then, we describe some specific implementation of the so-called Delayed Acceptance MH (DA-MH) methods [9]. <|MaskedSetence|> <|MaskedSetence|> F... | **A**: For instance, we provide a generic description of Metropolis-Hastings (MH) schemes on an iterative surrogate.
**B**: We also introduce Noisy Deep Importance Sampling (N-DIS) which is a noisy version of the Deep IS method in [53].
**C**: Section 5 is devoted to some theoretical discussions regarding the choice... | ABC | ABC | ABC | ABC | Selection 4 |
(3)
The eigenvalues decay at least polynomially. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The empirical eigenvalues are simple to compute, so it is simple to validate this assumption with a diagnostic plot. Figure 2 verifies polynomial decay of the empirical eigenvalues in the real world application o... | **A**: A higher value of b𝑏bitalic_b corresponds to a lower effective dimension, better control of the variance of our estimator, and hence a faster rate.
**B**: Any bounded kernel satisfies (3) [Fischer and Steinwart, 2020, Lemma 10].
**C**: The limit b→∞→𝑏b\rightarrow\inftyitalic_b → ∞ gives an RKHS with finite d... | BAC | BAC | BAC | ABC | Selection 1 |
Analyzing policies beyond SAA to achieve rate-optimality.
In Section 5.1 we complete the picture for the pricing problem under Wasserstein heterogeneity. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> We combine this observation with a critical relation between the Wasserstein distance (in 1 dimension) of tw... | **A**: To derive our result, we leverage the structure of the objective function in pricing: while it is not continuous in general, it is ensured to be one-sided Lipschitz-continuous (when deflating the price).
**B**: We also show that this performance is rate-optimal.
To our understanding, analyzing these non-SAA pol... | CBA | ACB | CBA | CBA | Selection 4 |
One example of unrepresentative samples is when the distributions of some covariates (e.g. age and BMI) in the target population differ from those in the source population. When some of these covariates are effect modifiers (e.g. age and BMI may modify the effects of some medicine), the ATE in the target population c... | **A**: In practice there often exist some covariates that are available in the source, but not in the target.
**B**: The form of their second-order bias and the conditions required to ensure their asymptotic normality are unclear.
**C**: Most of these papers adopt the idea that we first estimate the probability for a... | ABC | CBA | CBA | CBA | Selection 4 |
Based on the generic observation above, we offer the B-Spline basis (de Boor, 2001, pp 87) defined on the unit interval on uniformly spaced break-points, composed onto a transformation that maps the feature to the unit interval. The number of break-points (a.k.a knots) is a system hyper-parameter which can be further... | **A**: Although a significant part of this work considers the B-Spline basis, the techniques we present can be used with an arbitrary basis.
.
**B**: Hence, we can closely approximate the optimal segmentized functions, without introducing sparsity issues.
**C**: Moreover, to make integration of our idea easier in a... | CBA | BCA | BCA | BCA | Selection 3 |
The general update rules allow for deriving GFE-based messages around alternative sub-models, including continuous-variable models and possibly chance-constrained models (van de Laar
et al., 2021). Additionally, the general message update results allow for a parametrised goal prior, which may me modelled by a secondary... | **A**: These backward messages ensure the unified optimisation of the full GFE objective, without resorting to distinct schedules for state estimation and free energy evaluation.
**B**: As limitations, we identified convergence issues in the message updates, which were addressed by an alternative update rule that can ... | BCA | ABC | ABC | ABC | Selection 2 |
We use R2superscript𝑅2R^{2}italic_R start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Maximum Across All Entries (max error) to assess performance of estimated outcome matrices. <|MaskedSetence|> We split the data into 90/10901090/1090 / 10 train/test sets at ... | **A**: Using the tuned hyperparameters, we ran both algorithms for 10 experimental runs using different train/test splits to calculate performance metrics..
**B**: We directly compare the distribution of estimated entries to the distribution of true outcomes to assess the
bias of the algorithms.
Experiment setup.
*... | BCA | CBA | BCA | BCA | Selection 1 |
<|MaskedSetence|> We amended their approach and make it compatible with the Polar tool (Moosbrugger et al., 2022). Specifically, we incorporated the approach of (Jasour et al., 2021) into Prob-solvable loops when updates involve trigonometric functions. This allows us to automatically compute the exact moments of any ... | **A**: We present the new methodological material in Sec. 5.
.
**B**: Moreover, we extended (Jasour et al., 2021) to include exponential updates.
**C**:
(i)
(Jasour et al., 2021) developed a method that obtains the exact time evolution of the moments of random states for a class of dynamical systems that depend on ... | CBA | CBA | CBA | BAC | Selection 2 |
In this study, we propose a novel probability-based objective (loss) function for the semi-supervised node classification (community detection) task using higher-order networks. The loss function is motivated by the intuition that nodes densely interconnected with edges in a given network are likely to exhibit similar... | **A**: In general, traditional SBM-generated networks differ significantly from many real-world networks.
**B**: In conjunction with the objective function, we use discrete potential theory to initialize the node probability distribution, specifically the solution to an appropriate Dirichlet boundary value problem on ... | CBA | CBA | ACB | CBA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> Such strategies could involve the use of ensemble learning such as the stacking of Breiman (1996), the super-learning (a weighted average the best predictions over different choices of ML algorithm) of van der
Laan et al. (2007), or selecting the best-performing learner on an appli... | **A**: While we do not rule out using LASSO (and indeed do so in both our simulation study and empirical application), our approach requires only minimal assumptions (like Lipschitz continuity) about the nuisance functions.
**B**: This is important because, in practice, many ML algorithms are difficult to tune and hav... | ACB | ACB | CBA | ACB | Selection 2 |
This is the premise of sequential designs for computer experiments (Santner et al.,, 2003; Gramacy and Lee,, 2009; Sacks et al.,, 1989). While space-filling designs based on Latin Hypercube Sampling (LHS) (Stein,, 1987) or minimax distance designs (Johnson et al.,, 1990) have been widely investigated, they are not su... | **A**: For such cases, criterion-based designs have been introduced in various domains, such as reliability analysis (Lee and Jung,, 2008; Du and Chen,, 2002; Agrell and Dahl,, 2021; Azzimonti et al.,, 2021; Dubourg et al.,, 2013; Cole et al.,, 2023), Bayesian optimization (Shahriari et al.,, 2015; Imani and Ghoreishi,... | ABC | ABC | ABC | BAC | Selection 3 |
We introduce a new method for causal discovery in structural vector autoregressive models. We assess whether there is a causal effect from one time series component to another for any given time lag. The method is computationally very efficient and has asymptotic type I error control against false causal discoveries. ... | **A**: In networks, additional effects can be inferred by the logic that an ancestor of an ancestor must be an ancestor.
**B**: However, the ordering of the test statistics still provides some indication of what could be true ancestors..
**C**: Our simulations show that this error control works well for finite time s... | CBA | CAB | CAB | CAB | Selection 4 |
Recent work by (Goel et al. 2024) benchmarks several state-of-the-art (SoTA) unlearning algorithms, such as SSD (Foster, Schoepf, and Brintrup 2024), CF-k (Goel et al. 2022) and SCRUB (Kurmanji, Triantafillou, and Triantafillou 2023), within the corrective unlearning framework.
Notably, ASSD (Schoepf, Foster, and Brint... | **A**: 2020), and noise transition matrix estimation (Zhu, Wang, and Liu 2022; Cheng et al.
**B**: We provide a brief Literature Survey on Label Noise Learning in the supplementary..
**C**: 2021), curriculum learning (Jiang et al.
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where (Zt)0≤t≤Tsubscriptsubscript𝑍𝑡0𝑡𝑇(Z_{t})_{0\leq t\leq T}( italic_Z start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT ) start_POSTSUBSCRIPT 0 ≤ italic_t ≤ italic_T end_POSTSUBSCRIPT is another standard Brownian motion. <|MaskedSetence|> <|MaskedSetence|> Here, ∇logpXt∇subscript𝑝subscript𝑋𝑡\nabla\log p_{X_{t}... | **A**: This approach leads to the popular DDPM sampler (Ho et al.,, 2020; Nichol and Dhariwal,, 2021).
**B**: Define Yt=Y~T−tsubscript𝑌𝑡subscript~𝑌𝑇𝑡Y_{t}=\widetilde{Y}_{T-t}italic_Y start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = over~ start_ARG italic_Y end_ARG start_POSTSUBSCRIPT italic_T - italic_t end_POSTS... | BCA | BAC | BCA | BCA | Selection 4 |
1.1 Jet physics
During high-energy particle collisions, such as those observed at the Large Hadron Collider (LHC) at CERN, collimated sprays of particles called jets are produced. The jet constituents are the observed final-state particles that hit the detector and are originated by a showering process (described by Q... | **A**: This representation, first suggested in Louppe et al.
**B**: Fig. 1 provides a schematic representation of this process.
This results in several possible latent topologies corresponding to a set of leaves.
**C**: (2019), connects jets physics with natural language processing (NLP) and biology.
.
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1.3 Organisation
This paper is organised as follows. In Section 2, we estimate the second-order of the large-deviation probabilities of the rare event that a sparse Erdős–Rényi random graph has a linear number of vertices in triangles, study the structure of the graph conditionally on this rare event, and provide proo... | **A**: We close in Section 5 with a discussion and a list of open problems.
.
**B**: In Section 3, we use these results, as well as the key insights developed in their proofs, to study exponential random graphs based on the number of vertices in triangles.
**C**: We show that, for appropriate parameter choices, such... | BAC | BCA | BCA | BCA | Selection 2 |
<|MaskedSetence|> While NFs have been successfully used for posterior approximation [16, 17, 18, 19, 20] and produce higher-quality samples, the requirement that the Jacobian of each transformation be simple to compute often requires a high number of transformations and, traditionally, these transformations do not alt... | **A**: More recent lines of work on injective flow models 21, 22, 23, 24, 25 address this limitation by allowing practitioners to use flows to learn lower dimensional manifolds from data, but most compression-capable flow models still fail to reach high generative performance on key benchmark image datasets (cf.
**B**... | BAC | BAC | BCA | BAC | Selection 1 |
<|MaskedSetence|> Instead of relying on an estimate of the full conditional distribution, we use off-the-shelf multi-output quantile regression and construct prediction set by thresholding the estimated conditional interquantile intervals. <|MaskedSetence|> This allows us to estimate the conditional probability densi... | **A**: To address these limitations, we propose conformal thresholded intervals (CTI), a conformal inference method that seeks the smallest possible prediction set.
**B**: Compared with conformal histogram regression (CHR) (Sesia and Romano 2021), which first partitions the response space into bins, CTI directly train... | ABC | ABC | CBA | ABC | Selection 1 |
This gap is particularly relevant in applications such as transductive conformal prediction on traffic networks. <|MaskedSetence|> <|MaskedSetence|> 2024; Zargarbashi, Antonelli, and Bojchevski 2023; Zhao, Kang, and Cheng 2024). <|MaskedSetence|> This challenge arises from two main aspects. First, the sum or avera... | **A**: Conformalized GNN can output a prediction set of each edge’s label, but the cost of a route, which is the sum of the labels of the edges on the route, cannot be directly obtained by applying conformal prediction to individual edges.
**B**: For example, existing Graph Neural Network (GNN) methods can predict the... | BCA | BCA | ABC | BCA | Selection 4 |
The Bayesian framework is routinely used to quantify uncertainties in physics-driven parameters and models, e.g. <|MaskedSetence|> In theory, Bayesian neural networks (BNNs), which learn a distribution over the NN parameters instead of a single point estimate, could provide uncertainty estimates on the outputs, alongs... | **A**: [10, 11, 12, 13] to cite only a few examples in mechanics.
**B**: In practice however, Bayesian inference of NNs is notoriously challenging due to the high dimensionality and non physicality of the parameter space, and the nontrivial relationship between the parameter and function spaces, which complicates both... | ABC | ABC | ACB | ABC | Selection 4 |
Our linear-regression-based estimator integrates seamlessly into algorithms for preference-based bandits with linear human utility functions [3, 31], enabling interactive learning systems to leverage response times for faster learning. We specifically integrated our estimator into the Generalized Successive Eliminatio... | **A**: To the best of our knowledge, this is the first work to integrate response times into bandits (and RL).
Section 2 introduces the preference-based linear bandit problem and the difference-based EZ diffusion model.
**B**: Section 5 presents empirical results for estimation and bandit learning.
**C**: Appendix ... | CAB | ABC | ABC | ABC | Selection 4 |
HSBC: The authors declare no conflict of interest. This paper was prepared for informative purposes and is not a product of HSBC Bank Plc. <|MaskedSetence|> Neither HSBC Bank Plc. <|MaskedSetence|> <|MaskedSetence|> | **A**: or its affiliates.
**B**: This document is not intended as investment research or investment advice; or a recommendation, offer, or solicitation for the purchase or sale of any security, financial instrument, financial product, or service; or to be used in any way for evaluating the merits of participating in a... | ACB | ABC | ACB | ACB | Selection 3 |
Conversation Selection.
Several studies have developed conversation selection methods in RLHF. Here, a conversation includes the prompt and their completions. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2023); Mukherjee et al. (2024) in several ways:
. | **A**: However, it differs from those proposed by Zhan et al.
**B**: Our approach belongs to the first category.
**C**: These approaches can be roughly divided into two categories: (i) design-based approaches (Zhan et al., 2023; Mukherjee et al., 2024), which use the D𝐷Ditalic_D-optimality design to select conversat... | CBA | ACB | CBA | CBA | Selection 4 |
We perform computational approaches to address the extent to which geometric latent spaces add value to the relevant dynamics in the original space. <|MaskedSetence|> <|MaskedSetence|> In particular, we attempt to quantify sensitivity to noise, extrapolation with scaling, insertion of triviality in initial data, and ... | **A**: As a consequence, we investigate the role of latent geometry and how the organization of different latent spaces has different empirical outcomes.
.
**B**: We use learning strategies to construct geometries and the corresponding flows, and so we develop empirical applications with comparisons to vanilla VAEs an... | CBA | CBA | ACB | CBA | Selection 4 |
<|MaskedSetence|> <|MaskedSetence|> It then computes the sampling decisions for all agents, and communicates these decisions to each agent. These decisions are disseminated in batches, allowing multiple agents to simultaneously sample points, parallelizing the optimization process [23], [24].
Centralized approaches... | **A**:
We are interested in multi-agent BO, where multiple agents can sample the objective function at a single timestep.
**B**: In this work, we study the distributed setting with constrained communication, in which at each round, agents send their sampled points to their neighbors and receive points sampled by thei... | ACB | BAC | ACB | ACB | Selection 4 |
This limitation has led to the development of amortized BED (Foster et al.,, 2021; Ivanova et al.,, 2021; Blau et al.,, 2022, 2023), a policy-based method which leverages a neural network policy trained on simulated experimental trajectories to quickly generate designs, as illustrated in Fig. 1(b). <|MaskedSetence|> A... | **A**: Given an experiment history, these policy-based methods determine the next experimental design through a single forward pass, significantly speeding up the design process.
**B**: Therefore, we introduce the concept of Decision Utility Gain (DUG) to guide experimental design to better align with the downstream o... | ACB | ACB | BAC | ACB | Selection 4 |
<|MaskedSetence|> 2018), APPNP (Klicpera, Bojchevski, and Gunnemann 2019)), fairness-aware GNNs (FairGNN (Dai and Wang 2023), FairSIN (Yang et al. 2024), FMP (Jiang et al. <|MaskedSetence|> 2024b)), and GTs (DIFFormer (Wu et al. 2023a), SGFormer (Wu et al. 2023b), Polynormer (Deng, Yue, and Zhang 2024), CoBFormer (Xi... | **A**:
Baselines
We use three types of baseline: GNNs (GCN (Kipf and Welling 2017), GAT (Velickovic et al.
**B**: 2024), FUGNN (Luo et al.
**C**: 2024), FairGT (Luo et al.
| ABC | ABC | ACB | ABC | Selection 4 |
<|MaskedSetence|> (2007), can be particularly advantageous when we have prior knowledge of certain features or structures within the data. A key motivation for using orthogonal projection regularization operators is that it allows us to directly control the components of the solution within the range and null space of... | **A**: By projecting onto a subspace that aligns with known properties of the data, we can tailor the regularization to preserve essential characteristics.
**B**: The following proposition formalizes these insights and provides further motivation for selecting the appropriate regularization strategy by extending the f... | CAB | CAB | CAB | CAB | Selection 2 |
<|MaskedSetence|> One of the novel features of the CKKS scheme is its use of a rescaling operation. <|MaskedSetence|> In homomorphic encryption, noise is introduced with each operation performed, and if it grows too large, it can prevent accurate decryption. The rescaling operation in CKKS helps mitigate this issue b... | **A**: This capability is particularly important for applications involving scientific computations, statistics, and machine learning algorithms that require handling of non-integer data.
It is also worth mentioning that these schemes fall under the umbrella of lattice-based cryptography.
**B**: This operation is cr... | CBA | CBA | CBA | CAB | Selection 2 |
To advance the development and use of model-based estimates, we see a need to develop material to help standardize the specification and communication of model assumptions. <|MaskedSetence|> [2022]). <|MaskedSetence|> <|MaskedSetence|> This approach, or other efforts, can be considered to help communicate model as... | **A**: We show how existing models for a variety of indicators can be written as TMMPs and how the TMMP-based description can be used to compare and contrast model assumptions.
**B**: This model class facilitates documentation of model assumptions in a standardized way and facilitates comparison across models.
**C**:... | CBA | CBA | ACB | CBA | Selection 2 |
<|MaskedSetence|> Physical (true) random number generators (TRNG) using physical devices is an active research field since the 1950s (L’Ecuyer, 2017). Currently used random number generators are often feasability-motivated free-running oscillators with randomness from electronic noise (Stipcevic & Koç, 2014). A very r... | **A**: In addition, sequential operations that scale with the number of bits reduce the achievable sampling speed.
**B**:
Hence, the research community focused on the development of efficient random number generators (L’Ecuyer, 1994) and their infrastructure (Tan et al., 2021; Nagasaka et al., 2018) shares similariti... | BCA | BCA | BAC | BCA | Selection 4 |
The proposed model is broadly applicable to various domains, including social interactions, biological systems (e.g., neural or protein interactions), and technological networks (e.g., the spread of computer viruses or resilience of infrastructure systems). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Iden... | **A**: By transforming the SIR model using dynamical survival analysis within the edge-based configuration network framework, the resulting system of equations captures the intricate dynamics of network-based interactions.
**B**: Despite the complexity of these interactions, the equations remain mathematically tractab... | ABC | ABC | ABC | CBA | Selection 3 |
<|MaskedSetence|> In apprenticeship learning, this posterior over rewards is then used to produce a policy that should perform well with respect to the unknown reward function.
However, getting human demonstrations requires scarce human time. Also, many risky situations where we would wish AI systems to behave espec... | **A**: There is one previous paper on active IRL with full trajectories [5] suggesting a heuristic acquisition function whose shortcomings can, however, completely prevent learning.
**B**: It then combines this prior with demonstration data from a human expert acting approximately optimally with respect to the unknown... | BCA | BCA | ABC | BCA | Selection 1 |
All algorithms, (ours, NTD, tensor LDA, and hybrid-LDA) successfully identify the two groups along the first mode, as well as the mixed membership of the middle 10 indices.
The results in the second mode are more mixed. <|MaskedSetence|> By contrast, Tucker-decomposition-based methods (ours in Figure 1, Non-negative T... | **A**: Hybrid LDA, shown in Figure 4, fails to correctly recognize clusters along the second mode.
**B**: Moreover, the core tensors derived from our method (TTM-HOSVD) in Figure 1 and Tensor-LDA in Figure 3 reveal clear interactions between clusters along all modes.
**C**: In particular, these methods show the first... | ABC | ABC | ABC | BAC | Selection 2 |
<|MaskedSetence|> Tibshirani (1996) made the first such connection for
lasso regression, the Bayesian side of which was more fully developed by Park and Casella (2008) and
Hans (2009, 2010). <|MaskedSetence|> (2012), Leng et al. (2014), Alhamzawi and Ali (2018), Kang et al. <|MaskedSetence|> (2019),. | **A**: (2019), and Wang et al.
**B**: Bayesian connections to the adaptive lasso (Zou, 2006) have been considered
by Griffin and Brown (2007, 2011), Alhamzawi et al.
**C**: The literature on the connection between Bayesian posterior modes and estimators described as solutions
to penalized optimization problems is qui... | CBA | CBA | CBA | BAC | Selection 3 |
Figure 4: The loss reduction was compared within epochs among three different updates: the alternating Tweedie regression algorithm with and without learning rate adjustment, and Adam update. <|MaskedSetence|> The three different update methods are Fisher scoring type update with and without learning rate adjustment,... | **A**: Further, note that the with or without learning rate adjustment updates’ loss values are small compared to that of the Adam update.
**B**: The updates with or without learning rate adjustment reach stable loss value quickly.
**C**: The results are from the first iteration and first row of data matrix in our Al... | ACB | CBA | CBA | CBA | Selection 3 |
In all the aforementioned examples, there are explicitly observed covariates or factors. <|MaskedSetence|> Unfortunately, such models cannot be applied to user-item or co-occurrence count matrix data arising from many practical applications. Examples include user-item or item-item co-occurrence data from online sho... | **A**: Therefore, in this paper, we consider shared parameter modeling of zero-inflated Gamma data using alternating regression.
**B**: Furthermore, the two parts of the model parameters are
pendicular to each other, allowing model estimation by fitting two separate regressions: binomial regression and Gamma regressi... | BCA | BCA | BCA | BCA | Selection 3 |
Next we compare the performance of all six algorithms STh, OTh, HTh, STh2, OTh2, and HTh2. Figure 4(a) presents the results for the SRD of mean test errors for these six algorithms. Among all six algorithms, the OTh has the smallest sum rank difference, which means that it is the best algorithm in terms of the test er... | **A**: The ranking results are significant in that the SRD values of all algorithms are below 5% significance level.
**B**: The STh2 and STh have larger SRD values.
**C**: Therefore, according to the SRD method the OTh is the best algorithm and the STh is the worst algorithm in terms of the test error if all six algo... | BCA | BCA | CBA | BCA | Selection 4 |
<|MaskedSetence|> Since sizeable precise estimates of nonlabor income effects are rare, many studies neglect to account for nonlabor income, arguing that it is known that the nonlabor income effect is small. However, this reasoning is at odds with the findings in Golosov et al. (2024). They purposely design a data set... | **A**: Thus, panel data normally used are not well designed to accurately capture the nonlabor income effect.
**B**: They report, “On average, an extra dollar of unearned income in a given period reduces household labor earnings by about 50 cents, decreases total labor taxes by 10 cents, and increases consumption expe... | ABC | ABC | ABC | ACB | Selection 1 |
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