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作者：Papaspiliopoulos, O.; Rossell, D.
作者单位：ICREA; Pompeu Fabra University
摘要：We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for blo...
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作者：Xiao, Qian; Xu, Hongquan
作者单位：University of California System; University of California Los Angeles
摘要：Maximin distance Latin hypercube designs are widely used in computer experiments, yet their construction is challenging. Based on number theory and finite fields, we propose three algebraic methods to construct maximin distance Latin squares as special Latin hypercube designs. We develop lower bounds on their minimum distances. The resulting Latin squares and related Latin hypercube designs have larger minimum distances than existing ones, and are especially appealing for high-dimensional appl...
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作者：Lee, Seunggeun; Sun, Wei; Wright, Fred A.; Zou, Fei
作者单位：University of Michigan System; University of Michigan; Fred Hutchinson Cancer Center; North Carolina State University; State University System of Florida; University of Florida
摘要：Unobserved environmental, demographic and technical factors can adversely affect the estimation and testing of the effects of primary variables. Surrogate variable analysis, proposed to tackle this problem, has been widely used in genomic studies. To estimate hidden factors that are correlated with the primary variables, surrogate variable analysis performs principal component analysis either on a subset of features or on all features, but weighting each differently. However, existing approach...
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作者：Ding, P.; Vanderweele, T. J.; Robins, J. M.
作者单位：University of California System; University of California Berkeley; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covaria...
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作者：Constantinou, P.; Kokoszka, P.; Reimherr, M.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Colorado State University System; Colorado State University Fort Collins; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Separability is a common simplifying assumption on the covariance structure of spatiotemporal functional data. We present three tests of separability, one a functional extension of the Monte Carlo likelihood method of Mitchell et al. (2006) and two based on quadratic forms. Our tests are based on asymptotic distributions of maximum likelihood estimators and do not require Monte Carlo simulation. The main theoretical contribution of this paper is the specification of the joint asymptotic distri...
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作者：Gao, Xin; Carroll, Raymond J.
作者单位：York University - Canada; Texas A&M University System; Texas A&M University College Station
摘要：We consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured. The goal is to select which predictors affect any of the responses, where the number of such informative predictors tends to infinity as the sample size increas...
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作者：Kosmidis, I.; Guolo, A.; Varin, C.
作者单位：University of London; University College London; University of Padua; Universita Ca Foscari Venezia
摘要：Random-effects models are frequently used to synthesize information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component c...
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作者：Dalal, Onkar; Rajaratnam, Bala
作者单位：Stanford University; University of California System; University of California Davis
摘要：Several methods have recently been proposed for estimating sparse Gaussian graphical models using l(1)-regularization on the inverse covariance or precision matrix. Despite recent advances, contemporary applications require even faster methods to handle ill-conditioned high-dimensional datasets. In this paper, we propose a new method for solving the sparse inverse covariance estimation problem using the alternating minimization algorithm, which effectively works as a proximal gradient algorith...
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作者：Ehm, W.; Ovcharov, E. Y.
作者单位：Heidelberg Institute for Theoretical Studies; Bulgarian Academy of Sciences
摘要：Decompositions of the score of a forecast represent useful tools for assessing its performance. We consider local score decompositions permitting detailed forecast assessments across a spectrum of conditions of interest. We derive corrections to the bias of the decomposition components in the framework of point forecasts of quantile-type functionals, and illustrate their performance by simulation. Related bias corrections have thus far only been known for squared error criteria.
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作者：Lam, Clifford; Feng, Phoenix; Hu, Charlie
作者单位：University of London; London School Economics & Political Science
摘要：Integrated covariance matrices arise in intraday models of asset returns, which allow volatility to change over the trading day. When the number of assets is large, the natural estimator of such a matrix suffers from bias due to extreme eigenvalues. We introduce a novel nonlinear shrinkage estimator for the integrated covariance matrix which shrinks the extreme eigenvalues of a realized covariance matrix back to an acceptable level, and enjoys a certain asymptotic efficiency when the number of...
