2022

最新文章

• Variable Selection in the Presence of Factors: A Model Selection Perspective AI翻译
  作者：Garcia-Donato, Gonzalo; Paulo, Rui
  作者单位：Universidad de Castilla-La Mancha; Universidade de Lisboa; Universidade de Lisboa
  摘要：In the context of a Gaussian multiple regression model, we address the problem of variable selection when in the list of potential predictors there are factors, that is, categorical variables. We adopt a model selection perspective, that is, we approach the problem by constructing a class of models, each corresponding to a particular selection of active variables. The methodology is Bayesian and proceeds by computing the posterior probability of each of these models. We highlight the fact that...
• Fast, Optimal, and Targeted Predictions Using Parameterized Decision Analysis AI翻译
  作者：Kowal, Daniel R.
  作者单位：Rice University
  摘要：Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often difficult to interpret and slow to compute. Instead, we design a class of parameterized actions for Bayesian decision analysis that produce opti...
• Nonparametric Tests of the Causal Null With Nondiscrete Exposures AI翻译
  作者：Westling, Ted
  作者单位：University of Massachusetts System; University of Massachusetts Amherst
  摘要：In many scientific studies, it is of interest to determine whether an exposure has a causal effect on an outcome. In observational studies, this is a challenging task due to the presence of confounding variables that affect both the exposure and the outcome. Many methods have been developed to test for the presence of a causal effect when all such confounding variables are observed and when the exposure of interest is discrete. In this article, we propose a class of nonparametric tests of the ...
• Cross-Validation for Correlated Data AI翻译
  作者：Rabinowicz, Assaf; Rosset, Saharon
  作者单位：Tel Aviv University
  摘要：K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-iid data. This article analyzes CV for correlated data. We present a criterion for suitability of standard CV in presence of correlations. When this criterion does not hold, we introduce a bias corrected CV esti...
• Irrational Exuberance: Correcting Bias in Probability Estimates AI翻译
  作者：James, Gareth M.; Radchenko, Peter; Rava, Bradley
  作者单位：University of Southern California; University of Sydney
  摘要：We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased estimates, selecting events corresponding to the most extreme probabilities can result in systematically underestimating the true level of uncertainty. We develop an empirical Bayes approach excess certainty adjusted probabilities (ECAP), using a variant of Tweedie's formula, which updates probability estimates to corr...
• Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework AI翻译
  作者：Shi, Chengchun; Wang, Xiaoyu; Luo, Shikai; Zhu, Hongtu; Ye, Jieping; Song, Rui
  作者单位：University of London; London School Economics & Political Science; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of North Carolina; University of North Carolina Chapel Hill; University of Michigan System; University of Michigan; North Carolina State University
  摘要：A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major challenges arise in online experiments of two-sided marketplace platforms (e.g., Uber) where there is only one unit that receives a sequence of treatments over time. In those experiments, the treatment at a given time impacts current outcome as well as future outcomes. The aim of this article is to introduce a reinforceme...
• An Introduction to Acceptance Sampling and SPC with R AI翻译
  作者：Choe, Youngjun
  作者单位：University of Washington; University of Washington Seattle
• Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design AI翻译
  作者：Hasegawa, Raiden B.; Small, Dylan S.
  作者单位：University of Pennsylvania
  摘要：Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable gold-standard case defi...
• Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior AI翻译
  作者：Zhang, Yan Dora; Naughton, Brian P.; Bondell, Howard D.; Reich, Brian J.
  作者单位：University of Hong Kong; North Carolina State University; University of Melbourne
  摘要：Prior distributions for high-dimensional linear regression require specifying a joint distribution for the unobserved regression coefficients, which is inherently difficult. We instead propose a new class of shrinkage priors for linear regression via specifying a prior first on the model fit, in particular, the coefficient of determination, and then distributing through to the coefficients in a novel way. The proposed method compares favorably to previous approaches in terms of both concentrat...
• Nonparametric Maximum Likelihood Methods for Binary Response Models With Random Coefficients AI翻译
  作者：Gu, Jiaying; Koenker, Roger
  作者单位：University of Toronto; University of London; University College London
  摘要：The venerable method of maximum likelihood has found numerous recent applications innonparametricestimation of regression and shape constrained densities. For mixture models the nonparametric maximum likelihood estimator (NPMLE) of Kiefer and Wolfowitz plays a central role in recent developments of empirical Bayes methods. The NPMLE has also been proposed by Cosslett as an estimation method for single index linear models for binary response with random coefficients. However, computational diff...