text stringlengths 0 4.09k |
|---|
Title: The Synthesis of Regression Slopes in Meta-Analysis |
Abstract: Research on methods of meta-analysis (the synthesis of related study results) has dealt with many simple study indices, but less attention has been paid to the issue of summarizing regression slopes. In part this is because of the many complications that arise when real sets of regression models are accumulat... |
Title: On the Distribution of the Adaptive LASSO Estimator |
Abstract: We study the distribution of the adaptive LASSO estimator (Zou (2006)) in finite samples as well as in the large-sample limit. The large-sample distributions are derived both for the case where the adaptive LASSO estimator is tuned to perform conservative model selection as well as for the case where the tuni... |
Title: Recursive Bias Estimation and $L_2$ Boosting |
Abstract: This paper presents a general iterative bias correction procedure for regression smoothers. This bias reduction schema is shown to correspond operationally to the $L_2$ Boosting algorithm and provides a new statistical interpretation for $L_2$ Boosting. We analyze the behavior of the Boosting algorithm applie... |
Title: Methods to integrate a language model with semantic information for a word prediction component |
Abstract: Most current word prediction systems make use of n-gram language models (LM) to estimate the probability of the following word in a phrase. In the past years there have been many attempts to enrich such language models with further syntactic or semantic information. We want to explore the predictive powers of... |
Title: Concerning Olga, the Beautiful Little Street Dancer (Adjectives as Higher-Order Polymorphic Functions) |
Abstract: In this paper we suggest a typed compositional seman-tics for nominal compounds of the form [Adj Noun] that models adjectives as higher-order polymorphic functions, and where types are assumed to represent concepts in an ontology that reflects our commonsense view of the world and the way we talk about it in ... |
Title: Information Width |
Abstract: Kolmogorov argued that the concept of information exists also in problems with no underlying stochastic model (as Shannon's information representation) for instance, the information contained in an algorithm or in the genome. He introduced a combinatorial notion of entropy and information $I(x:\sy)$ conveyed ... |
Title: On the Complexity of Binary Samples |
Abstract: Consider a class $\mH$ of binary functions $h: X\to\-1, +1\$ on a finite interval $X=[0, B]\subset \Real$. Define the \em sample width of $h$ on a finite subset (a sample) $S\subset X$ as $\w_S(h) \equiv \min_x\in S |\w_h(x)|$, where $\w_h(x) = h(x) \max\a\geq 0: h(z)=h(x), x-a\leq z\leq x+a\$. Let $_\ell$ be... |
Title: Automatic Text Area Segmentation in Natural Images |
Abstract: We present a hierarchical method for segmenting text areas in natural images. The method assumes that the text is written with a contrasting color on a more or less uniform background. But no assumption is made regarding the language or character set used to write the text. In particular, the text can contain... |
Title: Shallow Models for Non-Iterative Modal Logics |
Abstract: The methods used to establish PSPACE-bounds for modal logics can roughly be grouped into two classes: syntax driven methods establish that exhaustive proof search can be performed in polynomial space whereas semantic approaches directly construct shallow models. In this paper, we follow the latter approach an... |
Title: Covariance estimation for multivariate conditionally Gaussian dynamic linear models |
Abstract: In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of confidence bounds of the forecasts. We develop an on-line... |
Title: Posterior mean and variance approximation for regression and time series problems |
Abstract: This paper develops a methodology for approximating the posterior first two moments of the posterior distribution in Bayesian inference. Partially specified probability models, which are defined only by specifying means and variances, are constructed based upon second-order conditional independence, in order ... |
Title: Multivariate stochastic volatility with Bayesian dynamic linear models |
Abstract: This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate bet... |
Title: Multivariate control charts based on Bayesian state space models |
Abstract: This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predic... |
Title: Dynamic generalized linear models for non-Gaussian time series forecasting |
Abstract: The purpose of this paper is to provide a discussion, with illustrating examples, on Bayesian forecasting for dynamic generalized linear models (DGLMs). Adopting approximate Bayesian analysis, based on conjugate forms and on Bayes linear estimation, we describe the theoretical framework and then we provide de... |
Title: Forecasting with time-varying vector autoregressive models |
Abstract: The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart ... |
Title: Multivariate stochastic volatility using state space models |
Abstract: A Bayesian procedure is developed for multivariate stochastic volatility, using state space models. An autoregressive model for the log-returns is employed. We generalize the inverted Wishart distribution to allow for different correlation structure between the observation and state innovation vectors and we ... |
Title: Global Sensitivity Analysis of Stochastic Computer Models with joint metamodels |
Abstract: The global sensitivity analysis method, used to quantify the influence of uncertain input variables on the response variability of a numerical model, is applicable to deterministic computer code (for which the same set of input variables gives always the same output value). This paper proposes a global sensit... |
Title: Hierarchical Additive Modeling of Nonlinear Association with Spatial Correlations-An Application to Relate Alcohol Outlet Density and Neighborhood Assault Rates |
Abstract: Previous studies have suggested a link between alcohol outlets and assaultive violence. In this paper, we explore the effects of alcohol availability on assault crimes at the census tract level over time. The statistical analysis is challenged by several features of the data: (1) the effects of possible covar... |
Title: The distribution of the maximum of a first order moving average: the continuous case |
Abstract: We give the distribution of $M_n$, the maximum of a sequence of $n$ observations from a moving average of order 1. Solutions are first given in terms of repeated integrals and then for the case where the underlying independent random variables have an absolutely continuous density. When the correlation is pos... |
Title: The distribution of the maximum of a first order moving average: the discrete case |
Abstract: We give the distribution of $M_n$, the maximum of a sequence of $n$ observations from a moving average of order 1. Solutions are first given in terms of repeated integrals and then for the case where the underlying independent random variables are discrete. When the correlation is positive, $$ P(M_n \max^n_i=... |
Title: A Conversation with Ingram Olkin |
Abstract: Ingram Olkin was born on July 23, 1924 in Waterbury, Connecticut. His family moved to New York in 1934 and he graduated from DeWitt Clinton High School in 1941. He served three years in the Air Force during World War II and obtained a B.S. in mathematics at the City College of New York in 1947. After receivin... |
Title: V-fold cross-validation improved: V-fold penalization |
Abstract: We study the efficiency of V-fold cross-validation (VFCV) for model selection from the non-asymptotic viewpoint, and suggest an improvement on it, which we call ``V-fold penalization''. Considering a particular (though simple) regression problem, we prove that VFCV with a bounded V is suboptimal for model sel... |
Title: A New Family of Random Graphs for Testing Spatial Segregation |
Abstract: We discuss a graph-based approach for testing spatial point patterns. This approach falls under the category of data-random graphs, which have been introduced and used for statistical pattern recognition in recent years. Our goal is to test complete spatial randomness against segregation and association betwe... |
Title: Relative Density of the Random $r$-Factor Proximity Catch Digraph for Testing Spatial Patterns of Segregation and Association |
Abstract: Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximi... |
Title: The Use of Domination Number of a Random Proximity Catch Digraph for Testing Spatial Patterns of Segregation and Association |
Abstract: Priebe et al. (2001) introduced the class cover catch digraphs and computed the distribution of the domination number of such digraphs for one dimensional data. In higher dimensions these calculations are extremely difficult due to the geometry of the proximity regions; and only upper-bounds are available. In... |
Title: Bayesian Checking of the Second Levels of Hierarchical Models |
Abstract: Hierarchical models are increasingly used in many applications. Along with this increased use comes a desire to investigate whether the model is compatible with the observed data. Bayesian methods are well suited to eliminate the many (nuisance) parameters in these complicated models; in this paper we investi... |
Title: Comment: Bayesian Checking of the Second Levels of Hierarchical Models |
Abstract: We discuss the methods of Evans and Moshonov [Bayesian Analysis 1 (2006) 893--914, Bayesian Statistics and Its Applications (2007) 145--159] concerning checking for prior-data conflict and their relevance to the method proposed in this paper. [arXiv:0802.0743] |
Title: Comment: Bayesian Checking of the Second Levels of Hierarchical Models |
Abstract: Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743] |
Title: Comment: Bayesian Checking of the Second Levels of Hierarchical Models |
Abstract: Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743] |
Title: Comment: Bayesian Checking of the Second Level of Hierarchical Models: Cross-Validated Posterior Predictive Checks Using Discrepancy Measures |
Abstract: Comment: Bayesian Checking of the Second Level of Hierarchical Models [arXiv:0802.0743] |
Title: Rejoinder: Bayesian Checking of the Second Levels of Hierarchical Models |
Abstract: Rejoinder: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743] |
Title: A multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression |
Abstract: A variable group Y is assumed to depend upon R thematic variable groups X 1, >..., X R . We assume that components in Y depend linearly upon components in the Xr's. In this work, we propose a multiple covariance criterion which extends that of PLS regression to this multiple predictor groups situation. On thi... |
Title: Mod\'elisation factorielle des interactions entre deux ensembles d'observations : la m\'ethode PLS-FILM (Partial Least Squares Factor Interaction Linear Modelling) |
Abstract: In this work, we consider a data array encoding interactions between two sets of observations respectively referred to as "subjects" and "objects". Besides, descriptions of subjects and objects are available through two variable sets. We propose a geometrically grounded exploratory technique to analyze the in... |
Title: Data-driven calibration of penalties for least-squares regression |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.