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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...
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作者：Song, Qifan; Sun, Yan; Ye, Mao; Liang, Faming
作者单位：Purdue University System; Purdue University
摘要：Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-sc...
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作者：Nghiem, Linh H.; Byrd, Michael C.; Potgieter, Cornelis J.
作者单位：Australian National University; Southern Methodist University; Texas Christian University
摘要：Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known or estimable from replicate data. A generalized method of moments approach can be used to estimate model parameters in the absence of knowledge of the error distributions, but it requires the existence of a large number of model moments. In this paper, parameter estimation based on the phase function, a normalized version of the characteristic function, is considered. T...
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作者：Vovk, Vladimir; Wang, Ruodu
作者单位：University of London; Royal Holloway University London; University of Waterloo
摘要：This paper proposes general methods for the problem of multiple testing of a single hypothesis, with a standard goal of combining a number of p-values without making any assumptions about their dependence structure. A result by Ruschendorf (1982) and, independently, Meng (1993) implies that the p-values can be combined by scaling up their arithmetic mean by a factor of 2, and no smaller factor is sufficient in general. A similar result by Mattner about the geometric mean replaces 2 by e. Based...
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作者：Zhang, X.; Lee, C. E.; Shao, X.
作者单位：State University System of Florida; Florida State University; University of Tennessee System; University of Tennessee Knoxville; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Envelopes have been proposed in recent years as a nascent methodology for sufficient dimension reduction and efficient parameter estimation in multivariate linear models. We extend the classical definition of envelopes in to incorporate a nonlinear conditional mean function and a heteroscedastic error. Given any two random vectors and , we propose two new model-free envelopes, called the martingale difference divergence envelope and the central mean envelope, and study their relationships to t...
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作者：Li, Didong; Dunson, David B.
作者单位：Duke University; Duke University
摘要：Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to achieve accurate classification when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports. This is particularly true when training data are limited. To address this problem, we propose a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighbourhood of the fea...
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作者：Xie, Fangzheng; Xu, Yanxun
作者单位：Johns Hopkins University
摘要：We propose and prove the optimality of a Bayesian approach for estimating the latent positions in random dot product graphs, which we call posterior spectral embedding. Unlike classical spectral-based adjacency, or Laplacian spectral embedding, posterior spectral embedding is a fully likelihood-based graph estimation method that takes advantage of the Bernoulli likelihood information of the observed adjacency matrix. We develop a minimax lower bound for estimating the latent positions, and sho...
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作者：Li, Wei; Gu, Yuwen; Liu, Lan
作者单位：Renmin University of China; University of Connecticut; University of Minnesota System; University of Minnesota Twin Cities
摘要：For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any one of the multiple models is correctly specified, a property termed multiple robustness. This ...
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作者：Yiu, A.; Goudie, R. J. B.; Tom, B. D. M.
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Fully Bayesian inference in the presence of unequal probability sampling requires stronger structural assumptions on the data-generating distribution than frequentist semiparametric methods, but offers the potential for improved small-sample inference and convenient evidence synthesis. We demonstrate that the Bayesian exponentially tilted empirical likelihood can be used to combine the practical benefits of Bayesian inference with the robustness and attractive large-sample properties of freque...
