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作者：Lin, D. Y.; Zeng, D.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Genomewide association studies have become the primary tool for discovering the genetic basis of complex human diseases. Such studies are susceptible to the confounding effects of population stratification, in that the combination of allele-frequency heterogeneity with disease-risk heterogeneity among different ancestral subpopulations can induce spurious associations between genetic variants and disease. This article provides a statistically rigorous and computationally feasible solution to t...
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作者：Jing, Bing-Yi; Kong, Xin-Bing; Liu, Zhi
作者单位：Hong Kong University of Science & Technology; Lanzhou University; Xiamen University
摘要：It is widely accepted that the high-frequency data are contaminated by microstructure noise, whose effect on the statistical inference has been of increasing interest in the literature. Much of it, however, has focused on the integrated volatility. In this article, we investigate another important characteristic, namely, the jump activity index (JAI) of a discretely sampled semi-martingale corrupted by microstructure noise. We point out that ignoring the microstructure noise can have a disastr...
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作者：Magnus, Jan R.; Melenberg, Bertrand; Muris, Chris
作者单位：Tilburg University; University of Gottingen
摘要：Two effects largely determine global warming: the well-known greenhouse effect and the less well-known solar radiation effect. An increase in concentrations of carbon dioxide and other greenhouse gases contributes to global warming: the greenhouse effect. In addition, small particles, called aerosols, reflect and absorb sunlight in the atmosphere. More pollution causes an increase in aerosols, so that less sunlight reaches the Earth (global dimming). Despite its name, global dimming is primari...
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作者：Xie, Minge; Singh, Kesar; Strawderman, William E.
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：This article develops a unifying framework, as well as robust meta-analysis approaches, for combining studies from independent sources. The device used in this combination is a confidence distribution (CD), which uses a distribution function, instead of a point (point estimator) or an interval (confidence interval), to estimate a parameter of interest. A CD function contains a wealth of information for inferences, and it is a useful device for combining studies from different sources. The prop...
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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...
