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作者：Sugasawa, S.
作者单位：University of Tokyo
摘要：A two-stage normal hierarchical model called the Fay-Herriot model and the empirical Bayes estimator are widely used to obtain indirect and model-based estimates of means in small areas. However, the performance of the empirical Bayes estimator can be poor when the assumed normal distribution is misspecified. This article presents a simple modification that makes use of density power divergence and proposes a new robust empirical Bayes small area estimator. The mean squared error and estimated...
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作者：Miles, C. H.; Shpitser, I; Kanki, P.; Meloni, S.; Tchetgen, E. J. Tchetgen
作者单位：Columbia University; Johns Hopkins University; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
摘要：Path-specific effects constitute a broad class of mediated effects from an exposure to an outcome via one or more causal pathways along a set of intermediate variables. Most of the literature concerning estimation of mediated effects has focused on parametric models, with stringent assumptions regarding unmeasured confounding. We consider semiparametric inference of a path-specific effect when these assumptions are relaxed. In particular, we develop a suite of semiparametric estimators for the...
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作者：Zhang, Han; Deng, Lu; Schiffman, Mark; Qin, Jing; Yu, Kai
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
摘要：Meta-analysis has become a powerful tool for improving inference by gathering evidence from multiple sources. It pools summary-level data from different studies to improve estimation efficiency with the assumption that all participating studies are analysed under the same statistical model. It is challenging to integrate external summary data calculated from different models with a newly conducted internal study in which individual-level data are collected. We develop a novel statistical infer...
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作者：Fong, E.; Holmes, C. C.
作者单位：University of Oxford
摘要：In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through k-fold partitioning or leave-p-out subsampling. We show that the marginal likelihood is formally equivalent to exhaustive leave-p-out cross-validation averaged over all values of p and all held-out test sets whe...
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作者：Nishimura, Akihiko; Dunson, David B.; Lu, Jianfeng
作者单位：University of California System; University of California Los Angeles; Duke University; Duke University
摘要：Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation. In this article we present an extension that can efficiently explore target distributions with discontinuous densities. Our extension in particular enables efficient sampling from ordinal parameters through the embedding of probability mass functions into continuous spaces. We motivate our approach through a theory of discontinuous Hamiltonian dynamics and develop a corresponding numerical solver. The proposed so...
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作者：Duan, Leo L.; Young, Alexander L.; Nishimura, Akihiko; Dunson, David B.
作者单位：State University System of Florida; University of Florida; Duke University; University of California System; University of California Los Angeles
摘要：Prior information often takes the form of parameter constraints. Bayesian methods include such information through prior distributions having constrained support. By using posterior sampling algorithms, one can quantify uncertainty without relying on asymptotic approximations. However, sharply constrained priors are not necessary in some settings and tend to limit modelling scope to a narrow set of distributions that are tractable computationally. We propose to replace the sharp indicator func...
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作者：Liu, X.; Zheng, S.; Feng, X.
作者单位：Shanghai University of Finance & Economics; Northeast Normal University - China
摘要：We propose a novel estimator of error variance and establish its asymptotic properties based on ridge regression and random matrix theory. The proposed estimator is valid under both low- and high-dimensional models, and performs well not only in nonsparse cases, but also in sparse ones. The finite-sample performance of the proposed method is assessed through an intensive numerical study, which indicates that the method is promising compared with its competitors in many interesting scenarios.
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作者：Tan, Z.
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：Propensity scores are widely used with inverse probability weighting to estimate treatment effects in observational studies. We study calibrated estimation as an alternative to maximum likelihood estimation for fitting logistic propensity score models. We show that, with possible model misspecification, minimizing the expected calibration loss underlying the calibrated estimators involves reducing both the expected likelihood loss and a measure of relative errors between the limiting and true ...
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作者：Cohen, P. L.; Olson, M. A.; Fogarty, C. B.
作者单位：Massachusetts Institute of Technology (MIT)
摘要：We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manifest itself in effects on multiple outcome variables in a known direction. The test statistic can be thought of as the solution to an adversarial game, where the researcher determines the best linear combination of test statistics to combat nature's presentation of the worst-case pattern of hidden bias. The correspond...
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作者：Liu, Hanzhong; Yang, Yuehan
作者单位：Tsinghua University; Central University of Finance & Economics
摘要：Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem for a stratified experiment, and we prove that both the stratified difference-in-means estimat...
