524期

最新文章

• Tracking the Impact of Media on Voter Choice in Real Time: A Bayesian Dynamic Joint Model AI翻译
  作者：Pareek, Bhuvanesh; Ghosh, Pulak; Wilson, Hugh N.; Macdonald, Emma K.; Baines, Paul
  作者单位：Indian Institute of Management (IIM System); Indian Institute of Management Indore; Indian Institute of Management (IIM System); Indian Institute of Management Bangalore; Cranfield University
  摘要：Commonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents' exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers' perceptual ...
• Sparsity Oriented Importance Learning for High-Dimensional Linear Regression AI翻译
  作者：Ye, Chenglong; Yang, Yi; Yang, Yuhong
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; McGill University
  摘要：With now well-recognized nonnegligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection process, regardless of its sophistication. To improve reliability and reproducibility in model choice, one constructive approach is to make good use of a sound variable importance measure. Although interesting importance measures are available and increasingly used in data analysis, little theoretical justification has be...
• Modeling Random Effects Using Global-Local Shrinkage Priors in Small Area Estimation AI翻译
  作者：Tang, Xueying; Ghosh, Malay; Ha, Neung Soo; Sedransk, Joseph
  作者单位：Columbia University; State University System of Florida; University of Florida; University System of Maryland; University of Maryland College Park
  摘要：Small area estimation is becoming increasingly popular for survey statisticians. One very important program is Small Area Income and Poverty Estimation undertaken by the United States Bureau of the Census, which aims at providing estimates related to income and poverty based on American Community Survey data at the state level and even at lower levels of geography. This article introduces global-local (GL) shrinkage priors for random effects in small area estimation to capture wide area level ...
• Post-Selection Inference Following Aggregate Level Hypothesis Testing in Large-Scale Genomic Data AI翻译
  作者：Heller, Ruth; Chatterjee, Nilanjan; Krieger, Abba; Shi, Jianxin
  作者单位：Tel Aviv University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; University of Pennsylvania; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：In many genomic applications, hypotheses tests are performed for powerful identification of signals by aggregating test-statistics across units within naturally defined classes. Following class-level testing, it is naturally of interest to identify the lower level units which contain true signals. Testing the individual units within a class without taking into account the fact that the class was selected using an aggregate-level test-statistic, will produce biased inference. We develop a hypot...
• Sparse Pairwise Likelihood Estimation for Multivariate Longitudinal Mixed Models AI翻译
  作者：Hui, Francis K. C.; Mueller, Samuel; Welsh, A. H.
  作者单位：Australian National University; University of Sydney
  摘要：It is becoming increasingly common in longitudinal studies to collect and analyze data on multiple responses. For example, in the social sciences we may be interested in uncovering the factors driving mental health of individuals over time, where mental health is measured using a set of questionnaire items. One approach to analyzing such multi-dimensional data is multivariate mixed models, an extension of the standard univariate mixed model to handle multiple responses. Estimating multivariate...
• Tractable Bayesian Variable Selection: Beyond Normality AI翻译
  作者：Rossell, David; Rubio, Francisco J.
  作者单位：Pompeu Fabra University; University of London; London School of Hygiene & Tropical Medicine
  摘要：Bayesian variable selection often assumes normality, but the effects of model misspecification are not sufficiently understood. There are sound reasons behind this assumption, particularly for large p: ease of interpretation, analytical, and computational convenience. More flexible frameworks exist, including semi- or nonparametric models, often at the cost of some tractability. We propose a simple extension that allows for skewness and thicker-than-normal tails but preserves tractability. It ...
• Probabilities of Concurrent Extremes AI翻译
  作者：Dombry, Clement; Ribatet, Mathieu; Stoev, Stilian
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Marie et Louis Pasteur; Universite de Montpellier; University of Michigan System; University of Michigan
  摘要：The statistical modeling of spatial extremes has been an active area of recent research with a growing domain of applications. Much of the existing methodology, however, focuses on the magnitudes of extreme events rather than on their timing. To address this gap, this article investigates the notion of extremal concurrence. Suppose that daily temperatures are measured at several synoptic stations. We say that extremes are concurrent if record maximum temperatures occur simultaneously, that is,...
• Linear Hypothesis Testing in Dense High-Dimensional Linear Models AI翻译
  作者：Zhu, Yinchu; Bradic, Jelena
  作者单位：University of California System; University of California San Diego
  摘要：We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, that is, model sparsity or the loading vector representing the hypothesis. Providing asymptotically valid methods for testing general linear functions of the regression parameters in high-dimensions is extremely challengingespecially without making restrictive or unverifiable assumptions on the number of nonzero elements. We propo...
• Optimal Penalized Function-on-Function Regression Under a Reproducing Kernel Hilbert Space Framework AI翻译
  作者：Sun, Xiaoxiao; Du, Pang; Wang, Xiao; Ma, Ping
  作者单位：University System of Georgia; University of Georgia; Virginia Polytechnic Institute & State University; Purdue University System; Purdue University
  摘要：Many scientific studies collect data where the response and predictor variables are both functions of time, location, or some other covariate. Understanding the relationship between these functional variables is a common goal in these studies. Motivated from two real-life examples, we present in this article a function-on-function regression model that can be used to analyze such kind of functional data. Our estimator of the 2D coefficient function is the optimizer of a form of penalized least...
• Bayesian Estimation and Comparison of Moment Condition Models AI翻译
  作者：Chib, Siddhartha; Shin, Minchul; Simoni, Anna
  作者单位：Washington University (WUSTL); University of Illinois System; University of Illinois Urbana-Champaign; Institut Polytechnique de Paris; ENSAE Paris; Ecole Polytechnique; Centre National de la Recherche Scientifique (CNRS)
  摘要：In this article, we develop a Bayesian semiparametric analysis of moment condition models by casting the problem within the exponentially tilted empirical likelihood (ETEL) framework. We use this framework to develop a fully Bayesian analysis of correctly and misspecified moment condition models. We show that even under misspecification, the Bayesian ETEL posterior distribution satisfies the Bernstein-von Mises (BvM) theorem. We also develop a unified approach based on marginal likelihoods and...