539期

最新文章

• An Invitation to Sequential Monte Carlo Samplers AI翻译
  作者：Dai, Chenguang; Heng, Jeremy; Jacob, Pierre E.; Whiteley, Nick
  作者单位：ESSEC Business School; University of Bristol
  摘要：Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers a...
• Extremile Regression AI翻译
  作者：Daouia, Abdelaati; Gijbels, Irene; Stupfler, Gilles
  作者单位：Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics; KU Leuven; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
  摘要：Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the families of spectral ...
• LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing AI翻译
  作者：Cai, T. Tony; Sun, Wenguang; Xia, Yin
  作者单位：University of Pennsylvania; University of Southern California; Fudan University
  摘要：Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems...
• Discussion of Confidence Intervals for Nonparametric Empirical Bayes Analysis by Ignatiadis and Wager AI翻译
  作者：Ghosal, Subhashis
  作者单位：North Carolina State University
• Coverage Properties of Empirical Bayes Intervals: A Discussion of Confidence Intervals for Nonparametric Empirical Bayes Analysis by Ignatiadis and Wager AI翻译
  作者：Hoff, Peter
  作者单位：Duke University
• Markov Neighborhood Regression for High-Dimensional Inference AI翻译
  作者：Liang, Faming; Xue, Jingnan; Jia, Bochao
  作者单位：Purdue University System; Purdue University; Eli Lilly; Lilly Research Laboratories
  摘要：This article proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into a series of low-dimensional inference problems: For each regression coefficient beta(i), the confidence interval and p-value are computed by regressing on a subset of variables selected according to the conditional independence relations between...
• Rejoinder: Confidence Intervals for Nonparametric Empirical Bayes Analysis AI翻译
  作者：Ignatiadis, Nikolaos; Wager, Stefan
  作者单位：Stanford University; Stanford University
• Coupled Generation AI翻译
  作者：Dai, Ben; Shen, Xiaotong; Wong, Wing
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Stanford University; Stanford University
  摘要：Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this ar...
• Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks AI翻译
  作者：Liang, Tengyuan; Tran-Bach, Hai
  作者单位：University of Chicago; University of Chicago
  摘要：We use a connection between compositional kernels and branching processes via Mehler's formula to study deep neural networks. This new probabilistic insight provides us a novel perspective on the mathematical role of activation functions in compositional neural networks. We study the unscaled and rescaled limits of the compositional kernels and explore the different phases of the limiting behavior, as the compositional depth increases. We investigate the memorization capacity of the compositio...
• Discussion of Confidence Intervals for Nonparametric Empirical Bayes Analysis by Nikolaos Ignatiadis and Stefan Wager AI翻译
  作者：Efron, Bradley
  作者单位：Stanford University