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作者：Gu, Yuqi; Xu, Gongjun
作者单位：Columbia University; University of Michigan System; University of Michigan
摘要：Structured latent attribute models (SLAMs) are a family of discrete latent variable models widely used in education, psychology, and epidemiology to model multivariate categorical data. A SLAM assumes that multiple discrete latent attributes explain the dependence of observed variables in a highly structured fashion. Usually, the maximum marginal likelihood estimation approach is adopted for SLAMs, treating the latent attributes as random effects. The increasing scope of modern assessment data...
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作者：Han, Xiao; Tong, Xin; Fan, Yingying
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California
摘要：Based on a Gaussian mixture type model of K components, we derive eigen selection procedures that improve the usual spectral clustering algorithms in high-dimensional settings, which typically act on the top few eigenvectors of an affinity matrix (e.g., (XX)-X-T) derived from the data matrix X. Our selection principle formalizes two intuitions: (i) eigenvectors should be dropped when they have no clustering power; (ii) some eigenvectors corresponding to smaller spiked eigenvalues should be dro...
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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...
