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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：Drton, Mathias; Goia, Aldo
作者单位：University of Chicago; University of Eastern Piedmont Amedeo Avogadro
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作者：Rousseau, Judith; Chopin, Nicolas; Liseo, Brunero
作者单位：Institut Polytechnique de Paris; ENSAE Paris; Sapienza University Rome
摘要：A stationary Gaussian process is said to be long-range dependent (resp., anti-persistent) if its spectral density f(lambda) can be written as f(lambda) = vertical bar lambda vertical bar(-2d) g(vertical bar lambda vertical bar), where 0 < 1/2 (resp., -1/2 < 0), and g is continuous and positive. We propose a novel Bayesian nonparametric approach for the estimation of the spectral density of such processes. We prove posterior consistency for both d and g, under appropriate conditions on the prio...
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作者：Bercu, Bernard; Fraysse, Philippe
作者单位：Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Bordeaux
摘要：This paper is devoted to the parametric estimation of a shift together with the nonparametric estimation of a regression function in a semiparametric regression model. We implement a very efficient and easy to handle Robbins-Monro procedure. On the one hand, we propose a stochastic algorithm similar to that of Robbins-Monro in order to estimate the shift parameter. A preliminary evaluation of the regression function is not necessary to estimate the shift parameter. On the other hand, we make u...
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作者：Fienberg, Stephen E.; Rinaldo, Alessandro
作者单位：Carnegie Mellon University
摘要：We study maximum likelihood estimation in log-linear models under conditional Poisson sampling schemes. We derive necessary and sufficient conditions for existence of the maximum likelihood estimator (MLE) of the model parameters and investigate estimability of the natural and mean-value parameters under a nonexistent MLE. Our conditions focus on the role of sampling zeros in the observed table. We situate our results within the framework of extended exponential families, and we exploit the ge...
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作者：Letac, Gerard; Massam, Helene
作者单位：Universite de Toulouse; Universite Toulouse III - Paul Sabatier; York University - Canada
摘要：A standard tool for model selection in a Bayesian framework is the Bayes factor which compares the marginal likelihood of the data under two given different models. In this paper, we consider the class of hierarchical loglinear models for discrete data given under the form of a contingency table with multinomial sampling. We assume that the prior distribution on the loglinear parameters is the Diaconis-Ylvisaker conjugate prior, and the uniform is the prior distribution on the space of models....
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作者：Tibshirani, Ryan J.; Taylor, Jonathan
作者单位：Carnegie Mellon University; Stanford University
摘要：We derive the degrees of freedom of the lasso fit, placing no assumptions on the predictor matrix X. Like the well-known result of Zou, Hastie and Tibshirani [Ann. Statist. 35 (2007) 2173-2192], which gives the degrees of freedom of the lasso fit when X has full column rank, we express our result in terms of the active set of a lasso solution. We extend this result to cover the degrees of freedom of the generalized lasso fit for an arbitrary predictor matrix X (and an arbitrary penalty matrix ...
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作者：Ledoit, Olivier; Wolf, Michael
作者单位：University of Zurich
摘要：Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer from ill-conditioning. There already exists an extensive literature concerning improved estimators in such situations. In the absence of further knowledge about the structure of the true covariance matrix, the most successful approach so far, a...
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作者：Shao, Jun; Deng, Xinwei
作者单位：East China Normal University; University of Wisconsin System; University of Wisconsin Madison; Virginia Polytechnic Institute & State University
摘要：Because of the advance in technologies, modem statistical studies often encounter linear models with the number of explanatory variables much larger than the sample size. Estimation and variable selection in these high-dimensional problems with deterministic design points is very different from those in the case of random covariates, due to the identifiability of the high-dimensional regression parameter vector. We show that a reasonable approach is to focus on the projection of the regression...
