Biometrika

最新文章

• Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods AI翻译
  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...
• Bayesian constraint relaxation AI翻译
  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...
• Estimation of error variance via ridge regression AI翻译
  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.
• Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data AI翻译
  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 ...
• Multivariate one-sided testing in matched observational studies as an adversarial game AI翻译
  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...
• Regression-adjusted average treatment effect estimates in stratified randomized experiments AI翻译
  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...
• Miscellanea A note on the accuracy of adaptive Gauss-Hermite quadrature AI翻译
  作者：Jin, Shaobo; Andersson, Bjorn
  作者单位：Uppsala University; University of Oslo
  摘要：Numerical quadrature methods are needed for many models in order to approximate integrals in the likelihood function. In this note, we correct the error rate given by Liu & Pierce (1994) for integrals approximated with adaptive Gauss-Hermite quadrature and show that the approximation is less accurate than previously thought. We discuss the relationship between the error rates of adaptive Gauss-Hermite quadrature and Laplace approximation, and provide a theoretical explanation of simulation res...
• Diagnosing missing always at random in multivariate data AI翻译
  作者：Bojinov, Iavor I.; Pillai, Natesh S.; Rubin, Donald B.
  作者单位：Harvard University; Harvard University; Tsinghua University
  摘要：Models for analysing multivariate datasets with missing values require strong, often unassessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable, which is a two-fold assumption dependent on the mode of inference. The first part, which is the focus here, under the Bayesian and direct-likelihood paradigms requires that the missing data be missing at random; in contrast, the frequentist-likelihood paradigm demands that the missing data mech...
• Scalable inference for crossed random effects models AI翻译
  作者：Papaspiliopoulos, O.; Roberts, G. O.; Zanella, G.
  作者单位：ICREA; University of Warwick; Bocconi University; Bocconi University
  摘要：We develop methodology and complexity theory for Markov chain Monte Carlo algorithms used in inference for crossed random effects models in modern analysis of variance. We consider a plain Gibbs sampler and propose a simple modification, referred to as a collapsed Gibbs sampler. Under some balancedness conditions on the data designs and assuming that precision hyperparameters are known, we demonstrate that the plain Gibbs sampler is not scalable, in the sense that its complexity is worse than ...
• Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection AI翻译
  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...