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作者：Nie, X.; Wager, S.
作者单位：Stanford University; Stanford University
摘要：Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical applications, such as personalized medicine and optimal resource allocation. In this article we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. First, we estimate marginal effects and treatment propensities to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective ...
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作者：Broda, Simon A.; Zambrano, Juan Arismendi
作者单位：Maynooth University
摘要：This article presents exact and approximate expressions for tail probabilities and partial moments of quadratic forms in multivariate generalized hyperbolic random vectors. The derivations involve a generalization of the classic inversion formula for distribution functions (Gil-Pelaez, 1951). Two numerical applications are considered: the distribution of the two-stage least squares estimator and the expected shortfall of a quadratic portfolio.
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作者：Lei, Lihua; Bickel, Peter J.
作者单位：Stanford University; University of California System; University of California Berkeley
摘要：We propose the cyclic permutation test to test general linear hypotheses for linear models. The test is nonrandomized and valid in finite samples with exact Type I error a for an arbitrary fixed design matrix and arbitrary exchangeable errors, whenever 1/alpha is an integer and n/p >= 1/alpha - 1, where n is the sample size and p is the number of parameters. The test involves applying the marginal rank test to 1/alpha linear statistics of the outcome vector, where the coefficient vectors are d...
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作者：Guo, X.; Tang, C. Y.
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：We consider testing the covariance structure in statistical models. We focus on developing such tests when the random vectors of interest are not directly observable and have to be derived via estimated models. Additionally, the covariance specification may involve extra nuisance parameters which also need to be estimated. In a generic additive model setting, we develop and investigate test statistics based on the maximum discrepancy measure calculated from the residuals. To approximate the di...
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作者：Howard, S. R.; Pimentel, S. D.
作者单位：University of California System; University of California Berkeley
摘要：A sensitivity analysis in an observational study tests whether the qualitative conclusions of an analysis would change if we were to allow for the possibility of limited bias due to confounding. The design sensitivity of a hypothesis test quantifies the asymptotic performance of the test in a sensitivity analysis against a particular alternative. We propose a new, nonasymptotic, distribution-free test, the uniform general signed rank test, for observational studies with paired data, and examin...
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作者：Van den Boom, W.; Reeves, G.; Dunson, D. B.
作者单位：Yale NUS College; National University of Singapore; Duke University
摘要：Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to moderate-dimensional parameter in the presence of a high-dimensional or otherwise computationally challenging nuisance parameter. The focus is on regression models and the key idea is to separate the likelihood into two components through a rotation. One component involves only the nuisance parameters, which can the...
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作者：Zheng, Yao; Cheng, Guang
作者单位：University of Connecticut; Purdue University System; Purdue University
摘要：This paper develops a unified finite-time theory for the ordinary least squares estimation of possibly unstable and even slightly explosive vector autoregressive models under linear restrictions, with the applicable region rho(A) <= 1 + c/n, where rho(A) is the spectral radius of the transition matrix A in the VAR(1) representation, n is the time horizon and c > 0 is a universal constant. The linear restriction framework encompasses various existing models such as banded/network vector autoreg...
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作者：Jeong, Seonghyun; Ghosal, Subhashis
作者单位：Yonsei University; North Carolina State University
摘要：We study posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity. A mixture of a point mass at zero and a continuous distribution is used as the prior distribution on regression coefficients. In addition to the usual posterior, the fractional posterior, which is obtained by applying Bayes theorem with a fractional power of the likelihood, is also considered. The latter allows uniformity in posterior contraction over a larger subset o...
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作者：Jentsch, Carsten; Lee, Eun Ryung; Mammen, Enno
作者单位：Dortmund University of Technology; Sungkyunkwan University (SKKU); Ruprecht Karls University Heidelberg
摘要：We discuss Poisson reduced-rank models for low-dimensional summaries of high-dimensional Poisson vectors that allow inference on the location of individuals in a low-dimensional space. We show that under weak dependence conditions, which allow for certain correlations between the Poisson random variables, the locations can be consistently estimated using Poisson maximum likelihood estimation. Moreover, we develop consistent rules for determining the dimension of the location from the discrete ...
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作者：Ahfock, D. C.; Astle, W. J.; Richardson, S.
作者单位：University of Cambridge; MRC Biostatistics Unit
摘要：Sketching is a probabilistic data compression technique that has been largely developed by the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original dataset, and this stochastic generation process makes them amenable to statistical analysis. W...
