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作者：Di Nardo, E.; McCullagh, P.; Senato, D.
作者单位：University of Basilicata; University of Chicago
摘要：Spectral sampling is associated with the group of unitary transformations acting on matrices in much the same way that simple random sampling is associated with the symmetric group acting on vectors. This parallel extends to symmetric functions, k-statistics and polykays. We construct spectral k-statistics as unbiased estimators of cumulants of trace powers of a suitable random matrix. Moreover we define normalized spectral polykays in such a way that when the sampling is from an infinite popu...
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作者：Field, Chris; Robinson, John
作者单位：Dalhousie University; University of Sydney
摘要：We consider the first serial correlation coefficient under an AR(1) model where errors are not assumed to be Gaussian. In this case it is necessary to consider bootstrap approximations for tests based on the statistic since the distribution of errors is unknown. We obtain saddle-point approximations for tail probabilities of the statistic and its bootstrap version and use these to show that the bootstrap tail probabilities approximate the true values with given relative errors, thus extending ...
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作者：Jiang, Jiming
作者单位：University of California System; University of California Davis
摘要：We give answer to an open problem regarding consistency of the maximum likelihood estimators (MLEs) in generalized linear mixed models (GLMMs) involving crossed random effects. The solution to the open problem introduces an interesting, nonstandard approach to proving consistency of the MLEs in cases of dependent observations. Using the new technique, we extend the results to MLEs under a general GLMM. An example is used to further illustrate the technique.
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作者：Xue, Lingzhou; Zou, Hui
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：A sparse precision matrix can be directly translated into a sparse Gaussian graphical model under the assumption that the data follow a joint normal distribution. This neat property makes high-dimensional precision matrix estimation very appealing in many applications. However, in practice we often face nonnormal data, and variable transformation is often used to achieve normality. In this paper we consider the nonparanormal model that assumes that the variables follow a joint normal distribut...
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作者：Ren, Zhao; Zhou, Harrison H.
作者单位：Yale University
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作者：Koul, Hira L.; Mueller, Ursula U.; Schick, Anton
作者单位：Michigan State University; Texas A&M University System; Texas A&M University College Station; State University of New York (SUNY) System; Binghamton University, SUNY
摘要：This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptotic behavior of complete case versions of established full data methods without lengthy proofs. The methodology is illustrated by analyzing three inference procedures for partially linear regression models with responses missing at random. We first ...
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作者：Anandkumar, Animashree; Tan, Vincent Y. F.; Huang, Furong; Willsky, Alan S.
作者单位：National University of Singapore; Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R)
摘要：We consider the problem of high-dimensional Ising (graphical) model selection. We propose a simple algorithm for structure estimation based on the thresholding of the empirical conditional variation distances. We introduce a novel criterion for tractable graph families, where this method is efficient, based on the presence of sparse local separators between node pairs in the underlying graph. For such graphs, the proposed algorithm has a sample complexity of n = Omega(J(min)(-2) log p), where ...
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作者：Lam, Clifford; Yao, Qiwei
作者单位：University of London; London School Economics & Political Science; Peking University
摘要：This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense that both the number of factors and the factor loadings are estimated in terms of an eigenanalysis for a nonnegative definite matrix, and is therefore applicable when the dimension of time series is on the order of a few thousands. Asymptotic properties of the proposed method are investigated under two settings: (i)...
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作者：Gehrmann, Helene; Lauritzen, Steffen L.
作者单位：University of Oxford
摘要：We study the problem of estimability of means in undirected graphical Gaussian models with symmetry restrictions represented by a colored graph. Following on from previous studies, we partition the variables into sets of vertices whose corresponding means are restricted to being identical. We find a necessary and sufficient condition on the partition to ensure equality between the maximum likelihood and least-squares estimators of the mean.
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作者：Kato, Kengo
作者单位：Hiroshima University
摘要：This paper studies estimation in functional linear quantile regression in which the dependent variable is scalar while the covariate is a function, and the conditional quantile for each fixed quantile index is modeled as a linear functional of the covariate. Here we suppose that covariates are discretely observed and sampling points may differ across subjects, where the number of measurements per subject increases as the sample size. Also, we allow the quantile index to vary over a given subse...
