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作者：Shen, Guohao; Chen, Kani; Huang, Jian; Lin, Yuanyuan
作者单位：Hong Kong Polytechnic University; Hong Kong University of Science & Technology; University of Iowa; Chinese University of Hong Kong
摘要：We propose a linearized maximum rank correlation estimator for the single-index model. Unlike the existing maximum rank correlation and other rank-based methods, the proposed estimator has a closed-form expression, making it appealing in theory and computation. The proposed estimator is robust to outliers in the response and its construction does not need knowledge of the unknown link function or the error distribution. Under mild conditions, it is shown to be consistent and asymptotically nor...
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作者：Rosenbaum, P. R.; Rubin, D. B.
作者单位：University of Pennsylvania; Harvard University
摘要：The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (), the propensity score is the unit-level conditional probability of assignment to treatment versus control given the observed covariates; so the propensity score explicitly does not involve any outcome variables, in co...
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作者：Chen, Jinsong; Li, Quefeng; Chen, Hua Yun
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of North Carolina; University of North Carolina Chapel Hill; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Generalized linear models often have high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional subvector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and are thus computationally expensive. Here, we ...
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作者：Kovacs, S.; Buehlmann, P.; Li, H.; Munk, A.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Gottingen
摘要：We propose seeded binary segmentation for large-scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of changepoints based on these candidates can be done in various ways, adapted to the problem at hand. The method is thus easy to adapt to many changepoint problems, ranging from univariate to high dimensional. Compared to recently popular random backg...
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作者：Hu, Jianhua; Huang, Jian; Liu, Xiaoqian; Liu, Xu
作者单位：Shanghai University of Finance & Economics; Hong Kong Polytechnic University; York University - Canada
摘要：This article investigates the statistical problem of response-variable selection with high-dimensional response variables and a diverging number of predictor variables with respect to the sample size in the framework of multivariate linear regression. A response best-subset selection model is proposed by introducing a 0-1 selection indicator for each response variable, and then a response best-subset selector is developed by introducing a separation parameter and a novel penalized least-square...
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作者：Wang, Shulei
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address th...
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作者：Klaassen, S.; Kueck, J.; Spindler, M.; Chernozhukov, V
作者单位：University of Hamburg; Massachusetts Institute of Technology (MIT)
摘要：Graphical models have become a popular tool for representing dependencies within large sets of variables and are crucial for representing causal structures. We provide results for uniform inference on high-dimensional graphical models, in which the number of target parameters d is potentially much larger than the sample size, under approximate sparsity. Our results highlight how graphical models can be estimated and recovered using modern machine learning methods in high-dimensional complex se...
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作者：Cui, Y.; Michael, H.; Tanser, F.; Tchetgen, E. Tchetgen
作者单位：National University of Singapore; University of Massachusetts System; University of Massachusetts Amherst; University of Lincoln; University of Pennsylvania
摘要：Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural m...
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作者：Chernozhukov, V; Newey, W. K.; Singh, R.
作者单位：Massachusetts Institute of Technology (MIT)
摘要：Debiased machine learning is a meta-algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i.e., scalar summaries, of machine learning algorithms. For example, an analyst may seek the confidence interval for a treatment effect estimated with a neural network. We present a non-asymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable...
