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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：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：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：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...
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作者：Zhou, Yan; Song, Peter X. -K.
作者单位：University of Michigan System; University of Michigan
摘要：This paper concerns regression methodology for assessing relationships between multi-dimensional response variables and covariates that are correlated within a network. To address analytical challenges associated with the integration of network topology into the regression analysis, we propose a hybrid quadratic inference method that uses both prior and data-driven correlations among network nodes. A Godambe information-based tuning strategy is developed to allocate weights between the prior a...
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作者：Mealli, Fabrizia; Rubin, Donald B.
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作者：Rao, Vinayak; Lin, Lizhen; Dunson, David B.
作者单位：Purdue University System; Purdue University; University of Texas System; University of Texas Austin; Duke University
摘要：We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea is a simple scheme to instantiate the rejected proposals preceding each data point. The resulting joint probability over observed and rejected variables can be much simpler than the marginal distribution over the observed variables, which often involves intractable integrals. We consider three problems: modelling flow-cytometry m...
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作者：Alexandrovich, G.; Holzmann, H.; Leister, A.
作者单位：Philipps University Marburg
摘要：Nonparametric identification and maximum likelihood estimation for finite-state hidden Markov models are investigated. We obtain identification of the parameters as well as the order of the Markov chain if the transition probability matrices have full-rank and are ergodic, and if the state-dependent distributions are all distinct, but not necessarily linearly independent. Based on this identification result, we develop a nonparametric maximum likelihood estimation theory. First, we show that t...
