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作者：Kai, Bo; Li, Runze; Zou, Hui
作者单位：College of Charleston; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
摘要：The complexity of semiparametric models poses new challenges to statistical inference and model selection that frequently arise from real applications. In this work, we propose new estimation and variable selection procedures for the semiparametric varying-coefficient partially linear model. We first study quantile regression estimates for the nonparametric varying-coefficient functions and the parametric regression coefficients. To achieve nice efficiency properties, we further develop a semi...
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作者：Arias-Castro, Ery; Candes, Emmanuel J.; Durand, Arnaud
作者单位：University of California System; University of California San Diego; Stanford University; Stanford University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Saclay
摘要：We consider the problem of detecting whether or not, in a given sensor network, there is a cluster of sensors which exhibit an unusual behavior. Formally, suppose we are given a set of nodes and attach a random variable to each node. We observe a realization of this process and want to decide between the following two hypotheses: under the null, the variables are i.i.d. standard normal; under the alternative, there is a cluster of variables that are i.i.d. normal with positive mean and unit va...
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作者：Wang, Lan
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Clustered binary data with a large number of covariates have become increasingly common in many scientific disciplines. This paper develops an asymptotic theory for generalized estimating equations (GEE) analysis of clustered binary data when the number of covariates grows to infinity with the number of clusters. In this large n, diverging p framework, we provide appropriate regularity conditions and establish the existence, consistency and asymptotic normality of the GEE estimator. Furthermor...
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作者：Zhang, Xinyu; Liang, Hua
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Rochester
摘要：We study model selection and model averaging in generalized additive partial linear models (GAPLMs). Polynomial spline is used to approximate nonparametric functions. The corresponding estimators of the linear parameters are shown to be asymptotically normal. We then develop a focused information criterion (FIC) and a frequentist model average (FMA) estimator on the basis of the quasi-likelihood principle and examine theoretical properties of the FIC and FMA. The major advantages of the propos...
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作者：Jiang, Ci-Ren; Wang, Jane-Ling
作者单位：University of California System; University of California Berkeley; University of California System; University of California Davis
摘要：A new single-index model that reflects the time-dynamic effects of the single index is proposed for longitudinal and functional response data, possibly measured with errors, for both longitudinal and time-invariant covariates. With appropriate initial estimates of the parametric index, the proposed estimator is shown to be root n-consistent and asymptotically normally distributed. We also address the nonparametric estimation of regression functions and provide estimates with optimal convergenc...
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作者：Lounici, Karim; Nickl, Richard
作者单位：University of Cambridge
摘要：We consider the statistical deconvolution problem where one observes n replications from the model Y = X + epsilon, where X is the unobserved random signal of interest and epsilon is an independent random error with distribution phi. Under weak assumptions on the decay of the Fourier transform of phi, we derive upper bounds for the finite-sample sup-norm risk of wavelet deconvolution density estimators f(n) for the density f of X, where f : R -> R is assumed to be bounded. We then derive lower...
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作者：Zhang, Mingyuan; Joffe, Marshall M.; Small, Dylan S.
作者单位：University of Pennsylvania; University of Pennsylvania
摘要：Most of the work on the structural nested model and g-estimation for causal inference in longitudinal data assumes a discrete-time underlying data generating process. However, in some observational studies, it is more reasonable to assume that the data are generated from a continuous-time process and are only observable at discrete time points. When these circumstances arise, the sequential randomization assumption in the observed discrete-time data, which is essential in justifying discrete-t...
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作者：Zhang, Chunming; Fan, Jianqing; Yu, Tao
作者单位：University of Wisconsin System; University of Wisconsin Madison; Princeton University; National University of Singapore
摘要：The multiple testing procedure plays an important role in detecting the presence of spatial signals for large-scale imaging data. Typically, the spatial signals are sparse but clustered. This paper provides empirical evidence that for a range of commonly used control levels, the conventional FDR procedure can lack the ability to detect statistical significance, even if the p-values under the true null hypotheses are independent and uniformly distributed; more generally, ignoring the neighborin...
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作者：Chi, Zhiyi
作者单位：University of Connecticut
摘要：The performance of multiple hypothesis testing is known to be affected by the statistical dependence among random variables involved. The mechanisms responsible for this, however, are not well understood. We study the effects of the dependence structure of a finite state hidden Markov model (HMM) on the likelihood ratios critical for optimal multiple testing on the hidden states. Various convergence results are obtained for the likelihood ratios as the observations of the HMM form an increasin...
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作者：Khare, Kshitij; Rajaratnam, Bala
作者单位：State University System of Florida; University of Florida; Stanford University
摘要：Gaussian covariance graph models encode marginal independence among the components of a multivariate random vector by means of a graph G. These models are distinctly different from the traditional concentration graph models (often also referred to as Gaussian graphical models or covariance selection models) since the zeros in the parameter are now reflected in the covariance matrix E, as compared to the concentration matrix Omega = Sigma(-1) The parameter space of interest for covariance graph...
