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作者：Chen, Shizhe; Witten, Daniela M.; Shojaie, Ali
作者单位：University of Washington; University of Washington Seattle
摘要：We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, ...
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作者：Zhu, Hongtu; Ibrahim, Joseph G.; Chen, Ming-Hui
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Connecticut
摘要：We investigate diagnostic measures for assessing the influence of observations and model misspecification on the Cox regression model when there are missing covariate data. Our diagnostics include case-deletion measures, conditional martingale residuals, and score residuals. The Q-distance is introduced to examine the effects of deleting individual observations on the estimates of finite- and infinite-dimensional parameters. Conditional martingale residuals are used to construct goodness-of-fi...
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作者：Fithian, William; Wager, Stefan
作者单位：Stanford University
摘要：We propose a semiparametric method for fitting the tail of a heavy-tailed population given a relatively small sample from that population and a larger sample from a related background population. We model the tail of the small sample as an exponential tilt of the better-observed large-sample tail, using a robust sufficient statistic motivated by extreme value theory. In particular, our method induces an estimator of the small-population mean, and we give theoretical and empirical evidence that...
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作者：Sun, Wenguang; Wei, Zhi
作者单位：University of Southern California; New Jersey Institute of Technology
摘要：We study how to separate signals from noisy data accurately and determine the patterns of the selected signals. Controlling the inflation of false positive errors is important in large-scale simultaneous inference but has not been addressed in the pattern recognition literature. We develop a decision-theoretic framework and formulate the sparse pattern recognition problem as a simultaneous inference problem with multiple decision trees. Oracle and adaptive classifiers are proposed for maximizi...
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作者：Zhang, Xiaoke; Wang, Jane-Ling
作者单位：University of Delaware; University of California System; University of California Davis
摘要：Both varying-coefficient and additive models have been studied extensively in the literature as extensions to linear models. They have also been extended to deal with functional response data. However, existing extensions are still not flexible enough to reflect the functional nature of the responses. In this paper, we extend varying-coefficient and additive models to obtain a much more flexible model and propose a simple algorithm to estimate its nonparametric additive and varying-coefficient...
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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 ...
