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作者：De Livera, Alysha M.; Hyndman, Rob J.; Snyder, Ralph D.
作者单位：University of Melbourne; Monash University
摘要：An innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects. The new framework incorporates Box-Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian e...
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作者：Dobra, Adrian; Lenkoski, Alex; Rodriguez, Abel
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Ruprecht Karls University Heidelberg; University of California System; University of California Santa Cruz
摘要：We introduce efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models. Our framework is based on the G-Wishart prior for the precision matrix associated with graphs that can be decomposable or non-decomposable. We extend our sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation in multivariate lattice data, with a special emphasis on the analysis of spatial data. The...
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作者：Fenske, Nora; Kneib, Thomas; Hothorn, Torsten
作者单位：University of Munich; Carl von Ossietzky Universitat Oldenburg
摘要：We investigated the risk factors for childhood malnutrition in India based on the 2005/2006 Demographic and Health Survey by applying a novel estimation technique for additive quantile regression. Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects, and, as a consequence, focus on average properties of the response. The use of such a regression model for analyzing childhood malnutrition in developing or tr...
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作者：Schweinberger, Michael
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：A number of discrete exponential family models for dependent data, first and foremost relational data, have turned out to be near-degenerate and problematic in terms of Markov chain Monte Carlo (MCMC) simulation and statistical inference. I introduce the notion of instability with an eye to characterize, detect, and penalize discrete exponential family models that are near-degenerate and problematic in terms of MCMC simulation and statistical inference. I show that unstable discrete exponentia...
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作者：Ding, Xiaobo; Wang, Qihua
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Yunnan University
摘要：Dimension reduction methods are useful for handling high-dimensional data. It is a common situation that responses of some subjects are not observed in practice. Generally, the missingness carries additional information about the central subspace. Here we propose a two-stage procedure known as the fusion-refinement (FR) procedure. In the first stage, we obtain a subspace including the central subspace by fusing information on regression and missingness. In the second stage, we refine the obtai...
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作者：Kang, Emily L.; Cressie, Noel
作者单位：University System of Ohio; Ohio State University
摘要：Spatial statistical analysis of massive amounts of spatial data can be challenging because computation of optimal procedures can break down. The Spatial Random Effects (SRE) model uses a fixed number of known but not necessarily orthogonal (multiresolutional) spatial basis functions, which gives a flexible family of nonstationary covariance functions, results in dimension reduction, and yields optimal spatial predictors whose computations are scalable. By modeling spatial data in a hierarchica...
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作者：Cai, Tony; Liu, Weidong; Luo, Xi
作者单位：University of Pennsylvania; Shanghai Jiao Tong University
摘要：This article proposes a constrained l(1) minimization method for estimating a sparse inverse covariance matrix based on a sample of n iid p-variate random variables. The resulting estimator is shown to have a number of desirable properties. In particular, the rate of convergence between the estimator and the true s-sparse precision matrix under the spectral norm is s root logp/n when the population distribution has either exponential-type tails or polynomial-type tails. We present convergence ...
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作者：Ding, Peng; Geng, Zhi; Yan, Wei; Zhou, Xiao-Hua
作者单位：University of Washington; University of Washington Seattle; Peking University; Peking University; US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System
摘要：In medical studies, there are many situations where the final outcomes are truncated by death, in which patients die before outcomes of interest are measured. In this article we consider identifiability and estimation of causal effects by principal stratification when some outcomes are truncated by death. Previous studies mostly focused on large sample bounds, Bayesian analysis, sensitivity analysis. In this article, we propose a new method for identifying the causal parameter of interest unde...
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作者：Laber, Eric B.; Murphy, Susan A.
作者单位：University of Michigan System; University of Michigan
摘要：The estimated test error of a learned classifier is the most commonly reported measure of classifier performance. However, constructing a high-quality point estimator of the test error has proved to be very difficult. Furthermore, common interval estimators (e.g., confidence intervals) are based on the point estimator of the test error and thus inherit all the difficulties associated with the point estimation problem. As a result, these confidence intervals do not reliably deliver nominal cove...
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作者：She, Yiyuan; Owen, Art B.
作者单位：State University System of Florida; Florida State University; Stanford University
摘要：This article studies the outlier detection problem from the standpoint of penalized regression. In the regression model, we add one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L-1 penalty yields a convex criterion, but fails to deliver a robust estimator. The L-1 penalty corresponds to soft thresholding. We introduce a thresholding (denoted by Theta) based iterative procedure for outlier detecti...
