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作者：Chen, Xiaohong
作者单位：Yale University
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作者：Wu, Colin O.; Tian, Xin
作者单位：National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
摘要：An objective of longitudinal analysis is to estimate the conditional distributions of an outcome variable through a regression model. The approaches based on modeling the conditional means are not appropriate for this task when the conditional distributions are skewed or cannot be approximated by a normal distribution through a known transformation. We study a class of time-varying transformation models and a two-step smoothing method for the estimation of the conditional distribution function...
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作者：Bondell, Howard D.; Stefanski, Leonard A.
作者单位：North Carolina State University
摘要：Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Although often not simultaneously attainable, we, develop and study a linear regression estimator that comes close. Efficiency is obtained from the estimator's close connection to generalized empirical likelihood, and its favorable robustness properties are obtained by constraining the associated sum of (weighted) squared residuals. We prove maximum attainable finite-sample replacement breakd...
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作者：Wu, Yuanshan; Yin, Guosheng
作者单位：Wuhan University; University of Hong Kong
摘要：Censored quantile regression offers a valuable complement to the traditional Cox proportional hazards model for survival analysis. Survival times tend to be right-skewed, particularly when there exists a substantial fraction of long-term survivors who are either cured or immune to the event of interest. For survival data with a cure possibility, we propose cure rate quantile regression under the common censoring scheme that survival times and censoring times are conditionally independent given...
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作者：Dias, Monica Costa; Ichimura, Hidehiko; van den Berg, Gerard J.
作者单位：University of London; London School Economics & Political Science; Universidade do Porto; IZA Institute Labor Economics; University of Tokyo; University of Tokyo; University of Mannheim; Vrije Universiteit Amsterdam
摘要：Matching methods for treatment evaluation based on a conditional independence assumption do not balance selective unobserved differences between treated and nontreated. We derive a simple correction term if there is an instrument that shifts the treatment probability to zero in specific cases. Policies with eligibility restrictions, where treatment is impossible if some variable exceeds a certain value, provide a natural application. In an empirical analysis, we exploit the age eligibility res...
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作者：Lin, Wei; Lv, Jinchi
作者单位：University of Pennsylvania; University of Southern California
摘要：High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by modern applications in high-throughput genomic data analysis and credit risk analysis. In this article, we propose a class of regularization methods for simultaneous variable selection and estimation in the additive hazards model, by combining the nonconcave penalized likelihood approach and the pseudoscore method. In a high-dimensional setting where the dimensionality can grow fast...
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作者：Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael
作者单位：University of Michigan System; University of Michigan; Federal Reserve System - USA; Federal Reserve Bank - New York; University of California System; University of California Berkeley
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作者：Delaigle, Aurore; Hall, Peter
作者单位：University of Melbourne
摘要：We consider classification of functional data when the training curves are not observed on the same interval. Different types of classifier are suggested, one of which involves a new curve extension procedure. Our approach enables us to exploit the information contained in the endpoints of these intervals by incorporating it in an explicit but flexible way. We study asymptotic properties of our classifiers, and show that, in a variety of settings, they can even produce asymptotically perfect c...
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作者：Taddy, Matt
作者单位：University of Chicago
摘要：Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article introduces a straightforward framework of sentiment-sufficient dimension reduction for text data. Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represent...
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作者：Liang, Faming; Song, Qifan; Yu, Kai
作者单位：Texas A&M University System; Texas A&M University College Station; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and co...
