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作者：Biron-Lattes, Miguel; Campbell, Trevor; Bouchard-Cote, Alexandre
作者单位：University of British Columbia
摘要：Simulated Tempering (ST) is an MCMC algorithm for complex target distributions that operates on a path between the target and a more amenable reference distribution. Crucially, if the reference enables iid sampling, ST is regenerative and can be parallelized across independent tours. However, the difficulty of tuning ST has hindered its widespread adoption. In this work, we develop a simple nonreversible ST (NRST) algorithm, a general theoretical analysis of ST, and an automated tuning procedu...
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作者：Painsky, Amichai
作者单位：Tel Aviv University
摘要：Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilities of unobserved events is a basic problem in statistics and related fields, which was extensively studied in the context of point estimation. In this work we introduce a novel interval estimation scheme for unobserved events. Our proposed framework applies selective inference, as we construct confidence intervals (...
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作者：Fan, Jianqing; Lou, Zhipeng; Wang, Weichen; Yu, Mengxin
作者单位：Princeton University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Hong Kong; University of Pennsylvania
摘要：Motivated by many applications such as online recommendations and individual choices, this article considers ranking inference of n items based on the observed data on the top choice among M randomly selected items at each trial. This is a useful modification of the Plackett-Luce model for M-way ranking with only the top choice observed and is an extension of the celebrated Bradley-Terry-Luce model that corresponds to M = 2. Under a uniform sampling scheme in which any M distinguished items ar...
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作者：Rasines, Daniel Garcia; Young, Alastair
作者单位：Imperial College London
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作者：Ahmed, Hanan; Einmahl, John H. J.; Zhou, Chen
作者单位：Tilburg University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
摘要：We consider extreme value analysis in a semi-supervised setting, where we observe, next to the n data on the target variable, n + m data on one or more covariates. This is called the semi-supervised model with n labeled and m unlabeled data. By exploiting the tail dependence between the target variable and the covariates, we derive estimators for the extreme value index and extreme quantiles of the target variable in this setting and establish their asymptotic behavior. Our estimators substant...
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作者：Lee, Seong-ho; Ma, Yanyuan; Zhao, Jiwei
作者单位：University of Seoul; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Wisconsin System; University of Wisconsin Madison
摘要：In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. We consider the setting when both outcome Y and covariate X are available from P but only X is available from Q, under the label shift assumption; that is, the conditional distribution of X given Y is the same in the two populations. To estimate the parameter of i...
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作者：Kuusela, Mikael
作者单位：Carnegie Mellon University
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作者：Qin, Jing; Liu, Yukun; Li, Moming; Huang, Chiung-Yu
作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); East China Normal University; East China Normal University; University of California System; University of California San Francisco
摘要：Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate shift, where the shift function needs to be estimated from data, many existing methods resort to data-splitting techniques. However, these approaches often lead to wider intervals and less reliable coverage rates, especially when dealing with finite sample sizes. To address these challenges, we propo...
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作者：Linero, Antonio R.
作者单位：University of Texas System; University of Texas Austin
摘要：Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary generalized BART models by introducing a very simple, tuning-paramete...
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作者：Lei, Jing; Oliveira, Natalia L.; Tibshirani, Ryan J.
作者单位：Carnegie Mellon University; Alphabet Inc.; Google Incorporated; University of California System; University of California Berkeley
