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作者：Andrews, Beth; Calder, Matthew; Davis, Richard A.
作者单位：Northwestern University; Columbia University
摘要：We consider maximum likelihood estimation for both causal and noncausal autoregressive time series processes with non-Gaussian alpha-stable noise. A nondegenerate limiting distribution is given for maximum likelihood estimators of the parameters of the autoregressive model equation and the parameters of the stable noise distribution. The estimators for the autoregressive parameters are n(1/alpha)-consistent and converge in distribution to the maximizer of a random function. The form of this li...
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作者：Fang, Fang; Hong, Quan; Shao, Jun
作者单位：Eli Lilly; Lilly Research Laboratories; University of Wisconsin System; University of Wisconsin Madison
摘要：Nonresponse is common in Surveys. When the response probability of a survey variable Y depends on Y through ail observed auxiliary categorical variable Z (i.e., the response probability of Y is conditionally independent of Y given Z), a simple method often used in practice is to use Z categories as imputation cells and construct estimators by imputing nonrespondents or reweighting respondents within each imputation cell. This simple method, however, is inefficient when some Z categories have s...
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作者：Wang, Dong; Chen, Song Xi
作者单位：University of Nebraska System; University of Nebraska Lincoln; Iowa State University; Peking University
摘要：We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wide-ranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter...
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作者：Bai, Zhidong; Jiang, Dandan; Yao, Jian-Feng; Zheng, Shurong
作者单位：Northeast Normal University - China; National University of Singapore; Northeast Normal University - China; Universite de Rennes; Universite de Rennes
摘要：In this paper, we give an explanation to the failure of two likelihood ratio procedures for testing about covariance matrices from Gaussian populations when the dimension p is large compared to the sample size n. Next, using recent central limit theorems for linear spectral statistics of sample covariance matrices and of random F-matrices, we propose necessary corrections for these LR tests to cope with high-dimensional effects. The asymptotic distributions of these corrected tests under the n...
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作者：Brien, C. J.; Bailey, R. A.
作者单位：University of South Australia; University of London
摘要：One aspect of evaluating the design for an experiment is the discovery of the relationships between subspaces of the data space. Initially we establish the notation and methods for evaluating an experiment with a single randomization. Starting with two structures, or orthogonal decompositions of the data space, we describe how to combine them to form the overall decomposition for a single-randomization experiment that is structure balanced. The relationships between the two structures are char...
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作者：Meier, Lukas; van de Geer, Sara; Buehlmann, Peter
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, for optimizing the penalized likelihood. Furthermore, we provide oracle results which yield asymptotic optimality of our estimator for high dimensional but sparse additive models. ...
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作者：Anderes, Ethan; Chatterjee, Sourav
作者单位：University of California System; University of California Davis; University of California System; University of California Berkeley
摘要：This paper proves fixed domain asymptotic results for estimating a smooth invertible transformation f:R-2 -> R-2 when observint, the deformed oil a dense,rid in a bounded, simply connected random field Z o f on a dense grid in a bounded, simply connected domain Omega, where Z is assumed to be an isotropic Gaussian random field on R-2. The estimate f is constructed on a simply connected domain U, such that (U) over bar subset of Omega and is defined using kernel smoothed quadratic variations, B...
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作者：Song, Rui; Kosorok, Michael R.; Fine, Jason P.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：We consider tests of hypotheses when the parameters are not identifiable under the null in semiparametric models, where regularity conditions for profile likelihood theory fail. Exponential average tests based on integrated profile likelihood are Constructed and shown to be asymptotically optimal under a weighted average power criterion with respect to a prior oil the nonidentifiable aspect of the model. These results extend existing results for parametric models, which involve more restrictiv...
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作者：Yuan, Ao
作者单位：Howard University
摘要：Bayesian and frequentist methods differ in many aspects, but share some basic optimality properties. In practice, there are situations in which one of the methods is more preferred by some criteria. We consider the case of inference about a Set Of Multiple parameters, which can be divided into two disjoint subsets. On one set, a frequentist method may be favored and on the other, the Bayesian. This motivates a joint estimation procedure in which some of the parameters are estimated Bayesian, a...
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作者：Hjort, Nils Lid; McKeague, Ian W.; Van Keilegom, Ingrid
作者单位：University of Oslo; Columbia University; Universite Catholique Louvain; Tilburg University
摘要：This article extends the scope of empirical likelihood methodology ill three directions: to allow for plug-in estimates Of nuisance parameters in estimating equations, slower than root n-rates of convergence, and settings in which there are a relatively large number of estimating equations compared to the sample size. Calibrating empirical likelihood confidence regions with plug-in is sometimes intractable due to the complexity of the asymptotics, so we introduce a bootstrap approximation that...
