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作者：Ke, Yuan; Li, Jialiang; Zhang, Wenyang
作者单位：Princeton University; National University of Singapore; University of York - UK
摘要：Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data wit...
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作者：Su, Yu-Ru; Wang, Jane-Ling
作者单位：Fred Hutchinson Cancer Center; University of California System; University of California Davis
摘要：In this paper, we investigate frailty models for clustered survival data that are subject to both left- and right-censoring, termed doubly-censored data. This model extends current survival literature by broadening the application of frailty models from right-censoring to a more complicated situation with additional left-censoring. Our approach is motivated by a recent Hepatitis B study where the sample consists of families. We adopt a likelihood approach that aims at the non parametric maximu...
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作者：Yoo, William Weimin; Ghosal, Subhashis
作者单位：Universite PSL; Universite Paris-Dauphine; North Carolina State University
摘要：In the setting of nonparametric multivariate regression with unknown error variance sigma(2), we study asymptotic properties of a Bayesian method for estimating a regression function f and its mixed partial derivatives. We use a random series of tensor product of B-splines with normal basis coefficients as a prior for f, and sigma is either estimated using the empirical Bayes approach or is endowed with a suitable prior in a hierarchical Bayes approach. We establish pointwise, L-2 and L-infini...
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作者：Jin, Jiashun; Wang, Wanjie
作者单位：Carnegie Mellon University; National University of Singapore
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作者：Xie, Xianchao; Kou, S. C.; Brown, Lawrence
作者单位：Harvard University; University of Pennsylvania
摘要：This paper discusses the simultaneous inference of mean parameters in a family of distributions with quadratic variance function. We first introduce a class of semiparametric/parametric shrinkage estimators and establish their asymptotic optimality properties. Two specific cases, the location-scale family and the natural exponential family with quadratic variance function, are then studied in detail. We conduct a comprehensive simulation study to compare the performance of the proposed methods...
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作者：Wang, Tengyao; Berthet, Quentin; Samworth, Richard J.
作者单位：University of Cambridge; California Institute of Technology
摘要：extremely popular dimension reduction technique for high-dimensional data. The theoretical challenge, in the simplest case, is to estimate the leading eigenvector of a population covariance matrix under the assumption that this eigenvector is sparse. An impressive range of estimators have been proposed; some of these are fast to compute, while others are known to achieve the mini-max optimal rate over certain Gaussian or sub-Gaussian classes. In this paper, we show that, under a widely-believe...
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作者：Cheng, Ming-Yen; Honda, Toshio; Li, Jialiang
作者单位：National Taiwan University; Hitotsubashi University; National University of Singapore
摘要：In semivarying coefficient modeling of longitudinal/clustered data, of primary interest is usually the parametric component which involves unknown constant coefficients. First, we study semiparametric efficiency bound for estimation of the constant coefficients in a general setup. It can be achieved by spline regression using the true within-subject covariance matrices, which are often unavailable in reality. Thus, we propose an estimator when the covariance matrices are unknown and depend onl...
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作者：Cavaliere, Giuseppe; Georgiev, Iliyan; Taylor, A. M. Robert
作者单位：University of Bologna; Universidade Nova de Lisboa; University of Essex
摘要：We extend the available asymptotic theory for autoregressive sieve estimators to cover the case of stationary and invertible linear processes driven by independent identically distributed (i.i.d.) infinite variance (IV) innovations. We show that the ordinary least squares sieve estimates, together with estimates of the impulse responses derived from these, obtained from an autoregression whose order is an increasing function of the sample size, are consistent and exhibit asymptotic properties ...
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作者：Liu, Hongcheng; Yao, Tao; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, they lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms ...
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作者：Cai, T. Tony; Zhang, Linjun
作者单位：University of Pennsylvania
