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作者：Wang, Xueqin; Pan, Wenliang; Hu, Wenhao; Tian, Yuan; Zhang, Heping
作者单位：Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen University; North Carolina State University; Yale University
摘要：Statistical inference on conditional dependence is essential in many fields including genetic association studies and graphical models. The classic measures focus on linear conditional correlations and are incapable of characterizing nonlinear conditional relationship including nonmonotonic relationship. To overcome this limitation, we introduce a nonparametric measure of conditional dependence for multivariate random variables with arbitrary dimensions. Our measure possesses the necessary and...
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作者：Wu, Yuanshan; Ma, Yanyuan; Yin, Guosheng
作者单位：University of Hong Kong; Wuhan University; University of South Carolina System; University of South Carolina Columbia
摘要：Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and correct...
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作者：Angrist, Joshua D.; Rokkanen, Miikka
作者单位：Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; Columbia University
摘要：In regression discontinuity (RD) studies exploiting an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than those required for identification at the cutoff. This article discusses RD identification and estimation away from the cutoff. Our identification strategy exploits the availability of dependent var...
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作者：Delaigle, Aurore; Zhou, Wen-Xin
作者单位：University of Melbourne; Princeton University
摘要：Group testing is a technique employed in large screening studies involving infectious disease, where individuals in the study are grouped before being observed. Parametric and nonparametric estimators of conditional prevalence have been developed in the group testing literature, in the case where the binary variable indicating the disease status is available only for the group, but the explanatory variable is observed for each individual. However, for reasons such as the high cost of assays, t...
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作者：Galvao, Antonio F.; Wang, Liang
作者单位：University of Iowa; University of Wisconsin System; University of Wisconsin Milwaukee
摘要：This article studies identification, estimation, and inference of general unconditional treatment effects models with continuous treatment under the ignorability assumption. We show identification of the parameters of interest, the dose-response functions, under the assumption that selection to treatment is based on observables. We propose a semiparametric two-step estimator, and consider estimation of the dose-response functions through moment restriction models with generalized residual func...
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作者：Chatterjee, A.; Lahiri, S. N.
作者单位：Indian Statistical Institute; Indian Statistical Institute Delhi; North Carolina State University
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作者：Simon, Noah; Tibshirani, Robert
作者单位：University of Washington; University of Washington Seattle; Stanford University; Stanford University
摘要：To date testing interactions in high dimensions is a challenging task. Existing methods often have issues with sensitivity to modeling assumptions and heavily asymptotic nominal p-values. To help alleviate these issues, we propose a permutation-based method for testing marginal interactions with a binary response. Our method searches for pairwise correlations that differ between classes. In this article, we compare our method on real and simulated data to the standard approach of running many ...
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作者：Zhang, Yichi; Laber, Eric B.
作者单位：North Carolina State University
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作者：Li, Sai; Mitra, Ritwik; Zhang, Cun-Hui
作者单位：Rutgers University System; Rutgers University New Brunswick; Princeton University
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作者：Frandsen, Brigham R.
作者单位：Brigham Young University
摘要：This article develops a nonparametric approach to identification and estimation of treatment effects on censored outcomes when treatment may be endogenous and have arbitrarily heterogenous effects. Identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework. The article proposes a censored quantile treatment effects estimator, derives its asymptotic distribution, and illustrates its perfo...
