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作者：Su, Zhihua; Cook, R. Dennis
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：In this article we propose a new model, called the inner envelope model, which leads to efficient estimation in the context of multivariate normal linear regression. The asymptotic distribution and the consistency of its maximum likelihood estimators are established. Theoretical results, simulation studies and examples all show that the efficiency gains can be substantial relative to standard methods and to the maximum likelihood estimators from the envelope model introduced recently by Cook e...
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作者：Favaro, S.; Lijoi, A.; Prunster, I.
作者单位：University of Turin; University of Pavia
摘要：Random probability measures are the main tool for Bayesian nonparametric inference, with their laws acting as prior distributions. Many well-known priors used in practice admit different, though equivalent, representations. In terms of computational convenience, stick-breaking representations stand out. In this paper we focus on the normalized inverse Gaussian process and provide a completely explicit stick-breaking representation for it. This result is of interest both from a theoretical view...
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作者：Papaspiliopoulos, Omiros; Pokern, Yvo; Roberts, Gareth O.; Stuart, Andrew M.
作者单位：Pompeu Fabra University; University of London; University College London; University of Warwick; University of Warwick
摘要：We consider estimation of scalar functions that determine the dynamics of diffusion processes. It has been recently shown that nonparametric maximum likelihood estimation is ill-posed in this context. We adopt a probabilistic approach to regularize the problem by the adoption of a prior distribution for the unknown functional. A Gaussian prior measure is chosen in the function space by specifying its precision operator as an appropriate differential operator. We establish that a Bayesian-Gauss...
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作者：Xu, Jing; Mackenzie, Gilbert
作者单位：University of London; University of Limerick
摘要：It can be more challenging to efficiently model the covariance matrices for multivariate longitudinal data than for the univariate case, due to the correlations arising between multiple responses. The positive-definiteness constraint and the high dimensionality are further obstacles in covariance modelling. In this paper, we develop a data-based method by which the parameters in the covariance matrices are replaced by unconstrained and interpretable parameters with reduced dimensions. The maxi...
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作者：Cox, D. R.; Kartsonaki, Christiana
作者单位：University of Oxford
摘要：Consider parametric models that are too complicated to allow calculation of a likelihood but from which observations can be simulated. We examine parameter estimators that are linear functions of a possibly large set of candidate features. A combination of simulations based on a fractional design and sets of discriminant analyses is then used to find an optimal estimator of the vector parameter and its covariance matrix. The procedure is an alternative to the approximate Bayesian computation s...
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作者：Chen, Li; Lin, D. Y.; Zeng, Donglin
作者单位：University of Kentucky; University of Kentucky; University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a graphical measure, the generalized negative predictive function, to quantify the predictive accuracy of covariates for survival time or recurrent event times. This new measure characterizes the event-free probabilities over time conditional on a thresholded linear combination of covariates and has direct clinical utility. We show that this function is maximized at the set of covariates truly related to event times and thus can be used to compare the predictive accuracy of differen...
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作者：Jung, Sungkyu; Dryden, Ian L.; Marron, J. S.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of South Carolina System; University of South Carolina Columbia; University of North Carolina; University of North Carolina Chapel Hill
摘要：A general framework for a novel non-geodesic decomposition of high-dimensional spheres or high-dimensional shape spaces for planar landmarks is discussed. The decomposition, principal nested spheres, leads to a sequence of submanifolds with decreasing intrinsic dimensions, which can be interpreted as an analogue of principal component analysis. In a number of real datasets, an apparent one-dimensional mode of variation curving through more than one geodesic component is captured in the one-dim...
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作者：Zhou, Ming; Kim, Jae Kwang
作者单位：Iowa State University
摘要：Panel attrition is frequently encountered in panel sample surveys. When it is related to the observed study variable, the classical approach of nonresponse adjustment using a covariate-dependent dropout mechanism can be biased. We consider an efficient method of estimation with monotone panel attrition when the response probability depends on the previous values of study variable as well as other covariates. Because of the monotone structure of the missing pattern, the response mechanism is mi...
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作者：Lu, W.; Goldberg, Y.; Fine, J. P.
作者单位：North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Penalization methods have been shown to yield both consistent variable selection and oracle parameter estimation under correct model specification. In this article, we study such methods under model misspecification, where the assumed form of the regression function is incorrect, including generalized linear models for uncensored outcomes and the proportional hazards model for censored responses. Estimation with the adaptive least absolute shrinkage and selection operator, lasso, penalty is pr...
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作者：Leng, Chenlei; Tang, Cheng Yong
作者单位：National University of Singapore
摘要：When a parametric likelihood function is not specified for a model, estimating equations may provide an instrument for statistical inference. Qin and Lawless (1994) illustrated that empirical likelihood makes optimal use of these equations in inferences for fixed low-dimensional unknown parameters. In this paper, we study empirical likelihood for general estimating equations with growing high dimensionality and propose a penalized empirical likelihood approach for parameter estimation and vari...
