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作者：Fronczyk, Kassandra; Kottas, Athanasios
作者单位：University of California System; University of California Santa Cruz
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作者：Patilea, Valentin; Raissi, Hamdi
作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
摘要：This article considers the volatility modeling for autoregressive univariate time series. A benchmark approach is the stationary autoregressive conditional heteroscedasticity (ARCH) model of Engle. Motivated by real data evidence, processes with nonconstant unconditional variance and ARCH effects have been recently introduced. We take into account this type of nonstationarity in variance and propose simple testing procedures for ARCH effects. Adaptive McLeod and Li's portmanteau and ARCH-LM te...
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作者：Luo, Shan; Chen, Zehua
作者单位：Shanghai Jiao Tong University; National University of Singapore
摘要：In this article, we propose a method called sequential Lasso (SLasso) for feature selection in sparse high-dimensional linear models. The SLasso selects features by sequentially solving partially penalized least squares problems where the features selected in earlier steps are not penalized. The SLasso uses extended BIC (EBIC) as the stopping rule. The procedure stops when EBIC reaches a minimum. The asymptotic properties of SLasso are considered when the dimension of the feature space is ultr...
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：In a nonrandomized or observational study, a weak association between receipt of the treatment and an outcome may be explained not as effects caused by the treatment but rather by a small bias in the assignment of individuals to treatment or control; however, a strong association may be explained as noncausal only by a large bias. The strength of the association between treatment and outcome is not uniform across the data from a study, and this motivates giving greater weight where the associa...
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作者：Wang, Bo; Shi, Jian Qing
作者单位：University of Leicester; Newcastle University - UK
摘要：In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial, Poisson, or other non-Gaussian distribution from an exponential family, while the covariates are mixed functional and scalar variables. The proposed model offers a nonparametric generalized concurrent regression method for functional data with multidimensional covariates, and provides a natural framework on modeling common mean struc...
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作者：Efron, Bradley
作者单位：Stanford University
摘要：Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and confidence intervals that take model selection into account. The methodology involves bagging, also known as bootstrap smoothing, to tame the erratic discontinuities of selection-based estimators. A useful new formula for the accuracy of bagging then provides standard errors for the smoothed estimators. Two examples, nonparametric and param...
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作者：Rosenblum, Michael; Liu, Han; Yen, En-Hsu
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Princeton University; University of Texas System; University of Texas Austin
摘要：We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such subpopulations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible ...
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作者：Wheeler, Matthew W.; Dunson, David B.; Pandalai, Sudha P.; Baker, Brent A.; Herring, Amy H.
作者单位：Centers for Disease Control & Prevention - USA; National Institute for Occupational Safety & Health (NIOSH); Duke University; Centers for Disease Control & Prevention - USA; National Institute for Occupational Safety & Health (NIOSH); University of North Carolina; University of North Carolina Chapel Hill
摘要：The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed in terms of differential equations. Motivated by studies of muscle activation, we propose a nonparametric Bayesian approach that takes into account mechanistic understanding of muscle phy...
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作者：Gupta, Shuva; Lahiri, S. N.
作者单位：North Carolina State University
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作者：Zhu, Hongtu; Khondker, Zakaria; Lu, Zhaohua; Ibrahim, Joseph G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a Bayesian generalized low-rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing i...
