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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：Tang, Runlong; Banerjee, Moulinath; Kosorok, Michael R.
作者单位：Princeton University; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
摘要：In this paper, we study the nonparametric maximum likelihood estimator for an event time distribution function at a point in the current status model with observation times supported on a grid of potentially unknown sparsity and with multiple subjects sharing the same observation time. This is of interest since observation time ties occur frequently with current status data. The grid resolution is specified as cn(-gamma) with c > 0 being a scaling constant and gamma > 0 regulating the sparsity...
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作者：Yuan, Ming
作者单位：University System of Georgia; Georgia Institute of Technology
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作者：Einmahl, John H. J.; Krajina, Andrea; Segers, Johan
作者单位：Tilburg University; University of Gottingen; Universite Catholique Louvain
摘要：Consider a random sample in the max-domain of attraction of a multivariate extreme value distribution such that the dependence structure of the attractor belongs to a parametric model. A new estimator for the unknown parameter is defined as the value that minimizes the distance between a vector of weighted integrals of the tail dependence function and their empirical counterparts. The minimization problem has, with probability tending to one, a unique, global solution. The estimator is consist...
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作者：Wu, Yuan; Zhang, Ying
作者单位：University of California System; University of California San Diego; University of Iowa
摘要：The analysis of the joint cumulative distribution function (CDF) with bivariate event time data is a challenging problem both theoretically and numerically. This paper develops a tensor spline-based sieve maximum likelihood estimation method to estimate the joint CDF with bivariate current status data. The I-splines are used to approximate the joint CDF in order to simplify the numerical computation of a constrained maximum likelihood estimation problem. The generalized gradient projection alg...
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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：Roman, Jorge Carlos; Hobert, James P.
作者单位：Vanderbilt University; State University System of Florida; University of Florida
摘要：Bayesian analysis of data from the general linear mixed model is challenging because any nontrivial prior leads to an intractable posterior density. However, if a conditionally conjugate prior density is adopted, then there is a simple Gibbs sampler that can be employed to explore the posterior density. A popular default among the conditionally conjugate priors is an improper prior that takes a product form with a flat prior on the regression parameter, and so-called power priors on each of th...
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作者：Vogt, Michael
作者单位：University of Cambridge
摘要：In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satisfied for a large class of nonlinear autoregressive processes ...
