-
作者：Zhao, Junlong; Zhou, Yang; Liu, Yufeng
作者单位：Beijing Normal University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：High-dimensional linear models are commonly used in practice. In many applications, one is interested in linear transformations beta(T)x of regression coefficients beta epsilon R-p, where x is a specific point and is not required to be identically distributed as the training data. One common approach is the plug-in technique which first estimates beta, then plugs the estimator in the linear transformation for prediction. Despite its popularity, estimation of beta canbe difficult for high-dimen...
-
作者：Bates, Stephen; Hastie, Trevor; Tibshirani, Robert
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Stanford University; Stanford University
摘要：Cross-validation is a widely used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, fit to the training data. We prove that this is not the case for the linear model fit by ordinary least squares; rather it estimates the average prediction error of models fit on other unseen training sets drawn from the same population. We further show that th...
-
作者：Hector, Emily C.; Reich, Brian J.
作者单位：North Carolina State University
摘要：Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using Max Stable Processes (MSPs) that are computationally prohibitive to fit for as few as a dozen observations. Supposed computationally-efficient approaches like the composite likelihood remain computationally burdensome with a few hundred observations. In this article, we propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers ...
-
作者：Wang, Yue; Nan, Bin; Kalbfleisch, John D. D.
作者单位：University of California System; University of California Irvine; University of Michigan System; University of Michigan
摘要：We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a...
-
作者：Zhang, Ting; Xu, Beibei
作者单位：University System of Georgia; University of Georgia
摘要：We consider the estimation and uncertainty quantification of the tail spectral density, which provide a foundation for tail spectral analysis of tail dependent time series. The tail spectral density has a particular focus on serial dependence in the tail, and can reveal dependence information that is otherwise not discoverable by the traditional spectral analysis. Understanding the convergence rate of tail spectral density estimators and finding rigorous ways to quantify their statistical unce...
-
作者：Xue, Kaijie; Yang, Jin; Yao, Fang
作者单位：Nankai University; National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD); Peking University
摘要：Most of existing methods of functional data classification deal with one or a few processes. In this work we tackle classification of high-dimensional functional data, in which each observation is potentially associated with a large number of functional processes, p, which is comparable to or even much larger than the sample size n. The challenge arises from the complex inter-correlation structures among multiple functional processes, instead of a diagonal correlation for a single process. Sin...
-
作者：Wang, Jia; Cai, Xizhen; Niu, Xiaoyue; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Williams College; Williams College
摘要：We consider a class of network models, in which the connection probability depends on ultrahigh-dimensional nodal covariates (homophily) and node-specific popularity (degree heterogeneity). A Bayesian method is proposed to select nodal features in both dense and sparse networks under a mild assumption on popularity parameters. The proposed approach is implemented via Gibbs sampling. To alleviate the computational burden for large sparse networks, we further develop a working model in which par...
-
作者：Qiu, Xing
作者单位：University of Rochester
-
作者：Deresa, Negera Wakgari; Keilegom, Ingrid Van
作者单位：KU Leuven
摘要：Most existing copula models for dependent censoring in the literature assume that the parameter defining the copula is known. However, prior knowledge on this dependence parameter is often unavailable. In this article we propose a novel model under which the copula parameter does not need to be known. The model is based on a parametric copula model for the relation between the survival time (T) and the censoring time (C), whereas the marginal distributions of T and C follow a semiparametric Co...
-
作者：Qi, Zhengling; Miao, Rui; Zhang, Xiaoke
作者单位：George Washington University; University of California System; University of California Irvine; George Washington University
摘要：Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish...
