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作者：Niu, Yabo; Ni, Yang; Pati, Debdeep; Mallick, Bani K.
作者单位：University of Houston System; University of Houston; Texas A&M University System; Texas A&M University College Station
摘要：In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets under-utilized in determining the joint dependency structure. In this article, we consider a Bayesian approach to model undirected graphs underlying heterogeneous multivariate observations with additional assistance from covariates. Building on product partiti...
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作者：Matsubara, Takuo; Knoblauch, Jeremias; Briol, Francois-Xavier; Oates, Chris. J.
作者单位：University of Edinburgh; University of London; University College London; Newcastle University - UK; Alan Turing Institute
摘要：Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalization constants requires summation over large or possibly infinite sets, which can be impractical. This article addresses this computational challenge through the development of a novel generalized Bayesian inference procedure suitable for discrete intractable likelihood. Inspired by recent methodological advances for continuous data, the main idea is to update beliefs abo...
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作者：Yang, Liuqing; Zhou, Yongdao; Fu, Haoda; Liu, Min-Qian; Zheng, Wei
作者单位：Nankai University; Eli Lilly; University of Tennessee System; University of Tennessee Knoxville; University of Tennessee System; University of Tennessee Knoxville
摘要：Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation toward the interpretable machine learning, node importance in social network, attribution models, etc. However, it could be very expensiv...
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作者：He, Yifan; Wu, Ruiyang; Zhou, Yong; Feng, Yang
作者单位：Chinese University of Hong Kong; New York University; East China Normal University; East China Normal University
摘要：Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without imposing strict const...
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作者：Shi, Chengchun; Zhou, Yunzhe; Li, Lexin
作者单位：University of London; London School Economics & Political Science; University of California System; University of California Berkeley
摘要：In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be time-dependent. We bui...
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作者：Boldea, Otilia; Magnus, Jan R.
作者单位：Tilburg University; Vrije Universiteit Amsterdam; Tinbergen Institute
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作者：Li, Ting; Shi, Chengchun; Lu, Zhaohua; Li, Yi; Zhu, Hongtu
作者单位：Shanghai University of Finance & Economics; University of London; London School Economics & Political Science; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：Many modern tech companies, such as Google, Uber, and Didi, use online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing platforms that involve...
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作者：Cui, Yifan; Hannig, Jan; Kosorok, Michael R.
作者单位：Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：Censored data, where the event time is partially observed, are challenging for survival probability estimation. In this article, we introduce a novel nonparametric fiducial approach to interval-censored data, including right-censored, current status, case II censored, and mixed case censored data. The proposed approach leveraging a simple Gibbs sampler has a useful property of being one size fits all, that is, the proposed approach automatically adapts to all types of noninformative censoring ...
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作者：Bing, Xin; Cheng, Wei; Feng, Huijie; Ning, Yang
作者单位：University of Toronto; Brown University; Microsoft; Cornell University
摘要：This article studies the inference of the regression coefficient matrix under multivariate response linear regressions in the presence of hidden variables. A novel procedure for constructing confidence intervals of entries of the coefficient matrix is proposed. Our method first uses the multivariate nature of the responses by estimating and adjusting the hidden effect to construct an initial estimator of the coefficient matrix. By further deploying a low-dimensional projection procedure to red...
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作者：Chakraborty, Saptarshi; Su, Zhihua
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; State University System of Florida; University of Florida
摘要：The envelope model aims to increase efficiency in multivariate analysis by using dimension reduction techniques. It has been used in many contexts including linear regression, generalized linear models, matrix/tensor variate regression, reduced rank regression, and quantile regression, and has shown the potential to provide substantial efficiency gains. Virtually all of these advances, however, have been made from a frequentist perspective, and the literature addressing envelope models from a ...
