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作者：Xu, Kelin; Guo, Wensheng; Xiong, Momiao; Zhu, Liping; Jin, Li
作者单位：Fudan University; University of Pennsylvania; University of Texas System; University of Texas Health Science Center Houston; Renmin University of China
摘要：Sufficient dimension reduction has been extensively explored in the context of independent and identically distributed data. In this article we generalize sufficient dimension reduction to longitudinal data and propose an estimating equation approach to estimating the central mean subspace. The proposed method accounts for the covariance structure within each subject and improves estimation efficiency when the covariance structure is correctly specified. Even if the covariance structure is mis...
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作者：Zeng, Donglin; Mao, Lu; Lin, D. Y.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood est...
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作者：Hiabu, M.; Mammen, E.; Martinez-Miranda, M. D.; Nielsen, J. P.
作者单位：City St Georges, University of London; Ruprecht Karls University Heidelberg
摘要：In this paper, in-sample forecasting is defined as forecasting a structured density to sets where it is unobserved. The structured density consists of one-dimensional in-sample components that identify the density on such sets. We focus on the multiplicative density structure, which has recently been seen as the underlying structure of non-life insurance forecasts. In non-life insurance, the in-sample area is defined as one triangle and the forecasting area as the triangle which, added to the ...
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作者：Kharroubi, S. A.; Sweeting, T. J.
作者单位：American University of Beirut; University of London; University College London
摘要：We use exponential tilting to obtain versions of asymptotic formulae for Bayesian computation that do not involve conditional maxima of the likelihood function, yielding a more stable computational procedure and significantly reducing computational time. In particular we present an alternative version of the Laplace approximation for a marginal posterior density. Implementation of the asymptotic formulae and a modified signed root based importance sampler are illustrated with an example.
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作者：Kim, Jae Kwang; Kwon, Yongchan; Paik, Myunghee Cho
作者单位：Iowa State University; Seoul National University (SNU)
摘要：Weighting adjustment is commonly used in survey sampling to correct for unit nonresponse. In cluster sampling, the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster-specific nonignorable missingness. Based on a parametric working model for the response mechanism that incorporates cluster-specific nonignorable missingness, we propose a method of weighting adjustment. We provide a consistent estimator of the mean or totals in cases where...
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作者：Schwarz, Katsiaryna; Krivobokova, Tatyana
作者单位：University of Gottingen
摘要：This article develops a unified framework to study the asymptotic properties of all periodic spline-based estimators, that is, of regression, penalized and smoothing splines. The explicit form of the periodic Demmler-Reinsch basis in terms of exponential splines allows the derivation of an expression for the asymptotic equivalent kernel on the real line for all spline estimators simultaneously. The corresponding bandwidth, which drives the asymptotic behaviour of spline estimators, is shown to...
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作者：Chang, Shu-Ching; Zimmerman, Dale L.
作者单位：University of Iowa
摘要：Antedependence models, also known as transition models, have proven to be useful for longitudinal data exhibiting serial correlation, especially when the variances and/or same-lag correlations are time-varying. Statistical inference procedures associated with normal antedependence models are well-developed and have many nice properties, but they are not appropriate for longitudinal data that exhibit considerable skewness. We propose two direct extensions of normal antedependence models to skew...
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作者：Prentice, R. L.
作者单位：Fred Hutchinson Cancer Center
摘要：The Clayton-Oakes bivariate failure time model is extended to dimensions m > 2 in a manner that allows unspecified marginal survivor functions for all dimensions less than m. Special cases that allow unspecified marginal survivor functions of dimension q or less with q < m, while making some provisions for dependencies of dimension greater than q, are also described.
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作者：Molstad, Aaron J.; Rothman, Adam J.
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：We propose a class of estimators of the multivariate response linear regression coefficient matrix that exploits the assumption that the response and predictors have a joint multivariate normal distribution. This allows us to indirectly estimate the regression coefficient matrix through shrinkage estimation of the parameters of the inverse regression, or the conditional distribution of the predictors given the responses. We establish a convergence rate bound for estimators in our class and we ...
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作者：Dombry, Clement; Engelke, Sebastian; Oesting, Marco
作者单位：Universite Marie et Louis Pasteur; Centre National de la Recherche Scientifique (CNRS); Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Universitat Siegen
摘要：Max-stable processes play an important role as models for spatial extreme events. Their complex structure as the pointwise maximum over an infinite number of random functions makes their simulation difficult. Algorithms based on finite approximations are often inexact and computationally inefficient. We present a new algorithm for exact simulation of a max-stable process at a finite number of locations. It relies on the idea of simulating only the extremal functions, that is, those functions i...
