Journal of the American Statistical Association

最新文章

• Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects AI翻译
  作者：Yadlowsky, Steve; Fleming, Scott; Shah, Nigam; Brunskill, Emma; Wager, Stefan
  作者单位：Alphabet Inc.; DeepMind; Stanford University; Stanford University; Stanford University; Stanford University
  摘要：There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules. RATE metrics are agnostic as to how the prioritization rules were derived, and only assess how well they identify individuals that benefit ...
• Statistical Analytics for Health Data Science with SAS and R AI翻译
  作者：Rahnavard, Ali; Wilson, Jeffrey R.; Chen, Ding-Geng; Peace, Karl E.
  作者单位：George Washington University
• Robust Leave-One-Out Cross-Validation for High-Dimensional Bayesian Models AI翻译
  作者：Silva, Luca Alessandro; Zanella, Giacomo
  作者单位：Bocconi University; Bocconi University; Bocconi University
  摘要：Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose asymptotic variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Ba...
• Unified Unconditional Regression for Multivariate Quantiles, M-Quantiles, and Expectiles AI翻译
  作者：Merlo, Luca; Petrella, Lea; Salvati, Nicola; Tzavidis, Nikos
  作者单位：European University of Rome; Sapienza University Rome; University of Pisa; University of Southampton
  摘要：In this article, we develop a unified regression approach to model unconditional quantiles, M-quantiles and expectiles of multivariate dependent variables exploiting the multidimensional Huber's function. To assess the impact of changes in the covariates across the entire unconditional distribution of the responses, we extend the work of Firpo, Fortin, and Lemieux by running a mean regression of the recentered influence function on the explanatory variables. We discuss the estimation procedure...
• An Automated Approach to Causal Inference in Discrete Settings AI翻译
  作者：Duarte, Guilherme; Finkelstein, Noam; Knox, Dean; Mummolo, Jonathan; Shpitser, Ilya
  作者单位：University of Pennsylvania; Johns Hopkins University; Princeton University; Princeton University
  摘要：Applied research conditions often make it impossible to point-identify causal estimands without untenable assumptions. Partial identification-bounds on the range of possible solutions-is a principled alternative, but the difficulty of deriving bounds in idiosyncratic settings has restricted its application. We present a general, automated numerical approach to causal inference in discrete settings. We show causal questions with discrete data reduce to polynomial programming problems, then pres...
• Optimal Design of Experiments on Riemannian Manifolds AI翻译
  作者：Li, Hang; Del Castillo, Enrique
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：The theory of optimal design of experiments has been traditionally developed on an Euclidean space. In this article, new theoretical results and an algorithm for finding the optimal design of an experiment located on a Riemannian manifold are provided. It is shown that analogously to the results in Euclidean spaces, D-optimal and G-optimal designs are equivalent on manifolds, and we provide a lower bound for the maximum prediction variance of the response evaluated over the manifold. In additi...
• Using SVD for Topic Modeling AI翻译
  作者：Ke, Zheng Tracy; Wang, Minzhe
  作者单位：Harvard University
  摘要：The probabilistic topic model imposes a low-rank structure on the expectation of the corpus matrix. Therefore, singular value decomposition (SVD) is a natural tool of dimension reduction. We propose an SVD-based method for estimating a topic model. Our method constructs an estimate of the topic matrix from only a few leading singular vectors of the data matrix, and has a great advantage in memory use and computational cost for large-scale corpora. The core ideas behind our method include a pre...
• Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process AI翻译
  作者：Shi, Chengchun; Zhu, Jin; Ye, Shen; Luo, Shikai; Zhu, Hongtu; Song, Rui
  作者单位：University of London; London School Economics & Political Science; Sun Yat Sen University; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：This article is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this article, we show that with some auxiliary variables that mediate the effect of actions on the...
• Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation AI翻译
  作者：Camerlenghi, Federico; Favaro, Stefano; Masoero, Lorenzo; Broderick, Tamara
  作者单位：University of Milano-Bicocca; Collegio Carlo Alberto; Bocconi University; University of Turin; Massachusetts Institute of Technology (MIT)
  摘要：There is a growing interest in the estimation of the number of unseen features, mostly driven by biological applications. A recent work brought out a peculiar property of the popular completely random measures (CRMs) as prior models in Bayesian nonparametric (BNP) inference for the unseen-features problem: for fixed prior's parameters, they all lead to a Poisson posterior distribution for the number of unseen features, which depends on the sampling information only through the sample size. CRM...
• Generalizing the Intention-to-Treat Effect of an Active Control from Historical Placebo-Controlled Trials: A Case Study of the Efficacy of Daily Oral TDF/FTC in the HPTN 084 Study AI翻译
  作者：He, Qijia; Gao, Fei; Dukes, Oliver; Delany-Moretlwe, Sinead; Zhang, Bo
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; Ghent University; University of Witwatersrand
  摘要：In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP) agent, to the FDA-approved daily oral tenofovir disoproxil fumarate plus emtricitabine ...