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作者：Panigrahi, Snigdha; Taylor, Jonathan
作者单位：University of Michigan System; University of Michigan; Stanford University
摘要：Several strategies have been developed recently to ensure valid inference after model selection; some of these are easy to compute, while others fare better in terms of inferential power. In this article, we consider a selective inference framework for Gaussian data. We propose a new method for inference through approximate maximum likelihood estimation. Our goal is to: (a) achieve better inferential power with the aid of randomization, (b) bypass expensive MCMC sampling from exact conditional...
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作者：Dorn, Jacob; Guo, Kevin
作者单位：Princeton University; Stanford University
摘要：Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible) assumption that all confounders have been measured. This article introduces a robust sensitivity analysis for IPW that estimates the range of treatment effects compatible with a given amount of unobserved confounding. The estimated range converges to the narrowest possible interval (under the given assump...
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作者：Zhou, Xingyu; Jiao, Yuling; Liu, Jin; Huang, Jian
作者单位：University of Iowa; Wuhan University; National University of Singapore
摘要：We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator, so that a random sample from the target conditional distribution can be obtained by transforming a sample drawn from a reference distribution. The conditional generator is estimated nonparametrically with neu...
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作者：Dagdoug, Mehdi; Goga, Camelia; Haziza, David
作者单位：Universite Marie et Louis Pasteur; University of Ottawa
摘要：In surveys, the interest lies in estimating finite population parameters such as population totals and means. In most surveys, some auxiliary information is available at the estimation stage. This information may be incorporated in the estimation procedures to increase their precision. In this article, we use random forests (RFs) to estimate the functional relationship between the survey variable and the auxiliary variables. In recent years, RFs have become attractive as National Statistical O...
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作者：Yang, Zihao; Qu, Tianyi; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference and provide excellent internal validity. However, they have also been criticized for questionable external validity, in the sense that the conclusion may not generalize well to a larger population. The randomized survey experiment is a design tool that can help mitigate this concern, by randomly sel...
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作者：Arellano, Manuel; Bonhomme, Stephane
作者单位：University of Chicago
摘要：We propose an optimal-transport-based matching method to nonparametrically estimate linear models with independent latent variables. The method consists in generating pseudo-observations from the latent variables, so that the Euclidean distance between the model's predictions and their matched counterparts in the data is minimized. We show that our nonparametric estimator is consistent, and we document that it performs well in simulated data. We apply this method to study the cyclicality of pe...
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作者：Yang, Guangyu; Zhang, Baqun; Zhang, Min
作者单位：University of Michigan System; University of Michigan; Shanghai University of Finance & Economics
摘要：The linear spline model is able to accommodate nonlinear effects while allowing for an easy interpretation. It has significant applications in studying threshold effects and change-points. However, its application in practice has been limited by the lack of both rigorously studied and computationally convenient method for estimating knots. A key difficulty in estimating knots lies in the nondifferentiability. In this article, we study influence functions of regular and asymptotically linear es...
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作者：Lin, Zhenhua; Lopes, Miles E.; Muller, Hans-Georg
作者单位：National University of Singapore; University of California System; University of California Davis
摘要：We propose a new approach to the problem of high-dimensional multivariate ANOVA via bootstrapping max statistics that involve the differences of sample mean vectors. The proposed method proceeds via the construction of simultaneous confidence regions for the differences of population mean vectors. It is suited to simultaneously test the equality of several pairs of mean vectors of potentially more than two populations. By exploiting the variance decay property that is a natural feature in rele...
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作者：Katsevich, Eugene; Sabatti, Chiara; Bogomolov, Marina
作者单位：University of Pennsylvania; Stanford University; Stanford University; Technion Israel Institute of Technology
摘要：Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the international classification of diseases (ICD), the directed acyclic graph structure of the gene ontology (GO), or the spatial structure in genome-wide association studies. In the context of multiple testing, the resulting relationships among hypotheses can create redundancies among rejections that hinder interpretability. This leads to the practice of filtering rejection sets ...
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作者：Zhang, Tao; Kato, Kengo; Ruppert, David
作者单位：Cornell University; Cornell University
摘要：In this article, we develop uniform inference methods for the conditional mode based on quantile regression. Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator. Building on high-dimensional Gaussian approximation techniques...
