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作者：Shi, Chengchun; Li, Lexin
作者单位：University of California System; University of California Berkeley
摘要：A central question in high-dimensional mediation analysis is to infer the significance of individual mediators. The main challenge is that the total number of potential paths that go through any mediator is super-exponential in the number of mediators. Most existing mediation inference solutions either explicitly impose that the mediators are conditionally independent given the exposure, or ignore any potential directed paths among the mediators. In this article, we propose a novel hypothesis ...
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作者：Paindaveine, Davy; Rasoafaraniaina, Josea; Verdebout, Thomas
作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
摘要：Multisample covariance estimation-that is, estimation of the covariance matrices associated with k distinct populations-is a classical problem in multivariate statistics. A common solution is to base estimation on the outcome of a test that these covariance matrices show some given pattern. Such a preliminary test may, for example, investigate whether or not the various covariance matrices are equal to each other (test of homogeneity), or whether or not they have common eigenvectors (test of c...
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作者：Zhang, Yingying; Wang, Huixia Judy; Zhu, Zhongyi
作者单位：East China Normal University; George Washington University; Fudan University
摘要：Threshold regression models are useful for identifying subgroups with heterogeneous parameters. The conventional threshold regression models split the sample based on a single and observed threshold variable, which enforces the threshold point to be equal for all subgroups of the population. In this article, we consider a more flexible single-index threshold model in the quantile regression setup, in which the sample is split based on a linear combination of predictors. We propose a new estima...
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作者：Chen, Xi; Liu, Weidong; Zhang, Yichen
作者单位：New York University; Shanghai Jiao Tong University; Purdue University System; Purdue University
摘要：This article studies distributed estimation and inference for a general statistical problem with a convex loss that could be nondifferentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward divide-and-conquer stochastic gradient descent approach. Our theory shows that there is a restriction on ...
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作者：Shin, Minsuk; Liu, Jun S.
作者单位：University of South Carolina System; University of South Carolina Columbia; Harvard University
摘要：Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the product of a Gaussian we...
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作者：Zhao, Jiwei; Ma, Yanyuan
作者单位：University of Wisconsin System; University of Wisconsin Madison; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propose a versatile estimation procedure where modeling of missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different scenarios. Comprehensive s...
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作者：Zhao, Sihai Dave; Biscarri, William
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Capital One Financial Corporation
摘要：Problems involving the simultaneous estimation of multiple parameters arise in many areas of theoretical and applied statistics. A canonical example is the estimation of a vector of normal means. Frequently, structural information about relationships between the parameters of interest is available. For example, in a gene expression denoising problem, genes with similar functions may have similar expression levels. Despite its importance, structural information has not been well-studied in the ...
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作者：Wang, Zixiao; Feng, Yi; Liu, Lin
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Shanghai Jiao Tong University; Shanghai Jiao Tong University
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作者：Barabesi, Lucio; Cerasa, Andrea; Cerioli, Andrea; Perrotta, Domenico
作者单位：University of Siena; European Commission Joint Research Centre; EC JRC ISPRA Site; University of Parma
摘要：Benford's law defines a probability distribution for patterns of significant digits in real numbers. When the law is expected to hold for genuine observations, deviation from it can be taken as evidence of possible data manipulation. We derive results on a transform of the significand function that provide motivation for new tests of conformance to Benford's law exploiting its sum-invariance characterization. We also study the connection between sum invariance of the first digit and the corres...
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作者：Zheng, Jiayin; Zheng, Yingye; Hsu, Li
作者单位：Fred Hutchinson Cancer Center
摘要：Predicting risks of chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a cohort, there is a great interest to apply the model to other cohorts. Due to potential discrepancy in baseline disease incidences between different cohorts and shifts in patient composition, the risk predicted by the model built in the source cohort often under- or over-estimates the risk in a new cohort. In this article, we assume the relative risks of predic...
