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作者：Camponovo, L.
作者单位：University of St Gallen
摘要：We study the validity of the pairs bootstrap for lasso estimators in linear regression models with random covariates and heteroscedastic error terms. We show that the naive pairs bootstrap does not provide a valid method for approximating the distribution of the lasso estimator. To overcome this deficiency, we introduce a modified pairs bootstrap procedure and prove its consistency. Finally, we consider the adaptive lasso and show that the modified pairs bootstrap consistently estimates the di...
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作者：Chen, Hua Yun
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：This paper points out an error in Davidov and Iliopoulos's (Biometrika 100, 778-80) proof of convergence of an iterative algorithm for the proportional likelihood ratio model. It is shown that the iterative algorithm increases the likelihood in each iteration and converges under mild additional conditions when the odds ratio function is bounded.
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作者：Ma, Ping; Huang, Jianhua Z.; Zhang, Nan
作者单位：University System of Georgia; University of Georgia; Texas A&M University System; Texas A&M University College Station
摘要：Smoothing splines provide flexible nonparametric regression estimators. However, the high computational cost of smoothing splines for large datasets has hindered their wide application. In this article, we develop a new method, named adaptive basis sampling, for efficient computation of smoothing splines in super-large samples. Except for the univariate case where the Reinsch algorithm is applicable, a smoothing spline for a regression problem with sample size n can be expressed as a linear co...
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作者：Yao, F.; Lei, E.; Wu, Y.
作者单位：University of Toronto; North Carolina State University
摘要：We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures o...
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作者：Shao, J.; Zhang, J.
作者单位：East China Normal University; University of Wisconsin System; University of Wisconsin Madison
摘要：We consider a linear mixed-effects model in which the response panel vector has missing components and the missing data mechanism depends on observed data as well as missing responses through unobserved random effects. Using a transformation of the data that eliminates the random effects, we derive asymptotically unbiased and normally distributed estimators of certain model parameters. Estimators of model parameters that cannot be estimated using the transformed data are also constructed, and ...
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作者：Kennedy, E. H.; Sjolander, A.; Small, D. S.
作者单位：University of Pennsylvania; Karolinska Institutet; University of Pennsylvania
摘要：Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent to standard estimat...
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作者：Cook, R. Dennis; Forzani, Liliana; Zhang, Xin
作者单位：University of Minnesota System; University of Minnesota Twin Cities; National University of the Littoral; State University System of Florida; Florida State University
摘要：We incorporate the nascent idea of envelopes (Cook et al., Statist. Sinica 20, 927-1010) into reduced-rank regression by proposing a reduced-rank envelope model, which is a hybrid of reduced-rank and envelope regressions. The proposed model has total number of parameters no more than either of reduced-rank regression or envelope regression. The resulting estimator is at least as efficient as both existing estimators. The methodology of this paper can be adapted to other envelope models, such a...
