-
作者：Basu, Pallavi; Cai, T. Tony; Das, Kiranmoy; Sun, Wenguang
作者单位：Tel Aviv University; University of Pennsylvania; Indian Statistical Institute; Indian Statistical Institute Kolkata; University of Southern California
摘要：The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This article studies weighted multiple testing in a decision-theoretical framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative...
-
作者：Wang, Lan; Zhou, Yu; Song, Rui; Sherwood, Ben
作者单位：University of Minnesota System; University of Minnesota Twin Cities; North Carolina State University; University of Kansas
摘要：Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies, and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This article studies a general framework for estimating the quantile-optimal treatment regime, which is o...
-
作者：Hahn, P. Richard; Martin, Ryan; Walker, Stephen G.
作者单位：University of Chicago; North Carolina State University; University of Texas System; University of Texas Austin
摘要：A Bayesian framework is attractive in the context of prediction, but a fast recursive update of the predictive distribution has apparently been out of reach, in part because Monte Carlo methods are generally used to compute the predictive. This article shows that online Bayesian prediction is possible by characterizing the Bayesian predictive update in terms of a bivariate copula, making it unnecessary to pass through the posterior to update the predictive. In standard models, the Bayesian pre...
-
作者：Tansey, Wesley; Koyejo, Oluwasanmi; Poldrack, Russell A.; Scott, James G.
作者单位：University of Texas System; University of Texas Austin; University of Illinois System; University of Illinois Urbana-Champaign; Stanford University; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：We present false discovery rate (FDR) smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false discovery rate at a given level. This results in increased power and cleaner spatial separation of signals fr...
-
作者：Keich, Uri; Noble, William Stafford
作者单位：University of Sydney; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：We consider the problem of controlling the false discovery rate (FDR) among discoveries from searching an incomplete database. This problem differs from the classical multiple testing setting because there are two different types of false discoveries: those arising from objects that have no match in the database and those that are incorrectly matched. We show that commonly used FDR controlling procedures are inadequate for this setup, a special case of which is tandem mass spectrum identificat...
-
作者：Zhou, Quan; Guan, Yongtao
作者单位：Baylor College of Medicine
摘要：We show that under the null, the is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and the normal prior. Our results have three immediate impacts. First, we can compute analytically a p-value associated with a Bayes factor without the need of permutation. We provide a software package that can evaluate t...
-
作者：Santacatterina, Michele; Bottai, Matteo
作者单位：Karolinska Institutet
摘要：Probability weights are used in many areas of research including complex survey designs, missing data analysis, and adjustment for confounding factors. They are useful analytic tools but can lead to statistical inefficiencies when they contain outlying values. This issue is frequently tackled by replacing large weights with smaller ones or by normalizing them through smoothing functions. While these approaches are practical, they are also prone to yield biased inferences. This article introduc...
-
作者：Lei, Jing; G'Sell, Max; Rinaldo, Alessandro; Tibshirani, Ryan J.; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a prediction band for the response variable using any estimator of the regression function. The resulting prediction band preserves the consistency properties of the original estimator under standard assumptions, while guaranteeing finite-sample marginal coverage even when these assumptions do not hold. We analyze and compar...
-
作者：Kim, Sungduk; Albert, Paul S.
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：Many researchers in biology and medicine have focused on trying to understand biological rhythms and their potential impact on disease. A common biological rhythm is circadian, where the cycle repeats itself every 24 hours. However, a disturbance of the circadian pattern may be indicative of future disease. In this article, we develop new statistical methodology for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a p...
-
作者：Wager, Stefan; Athey, Susan
作者单位：Stanford University
摘要：Many scientific and engineering challengesranging from personalized medicine to customized marketing recommendationsrequire an understanding of treatment effect heterogeneity. In this article, we develop a nonparametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect and have an asy...
