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作者：Pati, D.; Reich, B. J.; Dunson, D. B.
作者单位：Duke University; North Carolina State University
摘要：We consider geostatistical models that allow the locations at which data are collected to be informative about the outcomes. A Bayesian approach is proposed, which models the locations using a log Gaussian Cox process, while modelling the outcomes conditionally on the locations as Gaussian with a Gaussian process spatial random effect and adjustment for the location intensity process. We prove posterior propriety under an improper prior on the parameter controlling the degree of informative sa...
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作者：Poyiadjis, George; Doucet, Arnaud; Singh, Sumeetpal S.
作者单位：University of British Columbia; University of Cambridge
摘要：Particle methods are popular computational tools for Bayesian inference in nonlinear non-Gaussian state space models. For this class of models, we present two particle algorithms to compute the score vector and observed information matrix recursively. The first algorithm is implemented with computational complexity O(N) and the second with complexity O(N-2), where N is the number of particles. Although cheaper, the performance of the O(N) method degrades quickly, as it relies on the approximat...
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作者：Papathomas, M.; Dellaportas, P.; Vasdekis, V. G. S.
作者单位：Imperial College London; Athens University of Economics & Business
摘要：We propose a novel methodology to construct proposal densities in reversible jump algorithms that obtain samples from parameter subspaces of competing generalized linear models with differing dimensions. The derived proposal densities are not restricted to moves between nested models and are applicable even to models that share no common parameters. We illustrate our methodology on competing logistic regression and log-linear graphical models, demonstrating how our suggested proposal densities...
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作者：Neuhaus, John M.; McCulloch, Charles E.
作者单位：University of California System; University of California San Francisco
摘要：In standard regression analyses of clustered data, one typically assumes that the expected value of the response is independent of cluster size. However, this is often false. For example, in studies of surgical interventions, investigators have frequently found surgery volume and outcomes to be related to the skill level of the surgeons. This paper examines the effect of ignoring response-dependent, informative, cluster sizes on standard analytical methods such as mixed-effects models and cond...
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作者：Chen, Hua Yun
作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Based on the odds ratio representation of a joint density, we propose a unified framework to study parameter identifiability in biased sampling designs. It is shown that most of these designs encountered in practice can be reformulated within the proposed framework and, as a result, the question of parameter identifiability can be largely clarified. Estimation of the identifiable parameters is considered and traditional results on the equivalence of the prospective and retrospective likelihood...
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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：We introduce the partial envelope model, which leads to a parsimonious method for multivariate linear regression when some of the predictors are of special interest. It has the potential to achieve massive efficiency gains compared with the standard model in the estimation of the coefficients for the selected predictors. The partial envelope model is a variation on the envelope model proposed by Cook et al. (2010) but, as it focuses on part of the predictors, it has looser restrictions and can...
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作者：Farcomeni, A.
作者单位：Sapienza University Rome
摘要：We introduce a general class of capture-recapture models in which capture probabilities depend on capture history. We discuss constrained versions of the saturated model based on equality constraints. Inference can be performed through a simple estimating equation. The approach is illustrated on a dataset concerning Great Copper butterflies in Willamette Valley of Oregon.
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作者：Kim, Jae Kwang
作者单位：Iowa State University
摘要：Parametric fractional imputation is proposed as a general tool for missing data analysis. Using fractional weights, the observed likelihood can be approximated by the weighted mean of the imputed data likelihood. Computational efficiency can be achieved using the idea of importance sampling and calibration weighting. The proposed imputation method provides efficient parameter estimates for the model parameters specified in the imputation model and also provides reasonable estimates for paramet...
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作者：Preston, S. P.; Wood, Andrew T. A.
作者单位：University of Nottingham
摘要：Working within the framework of a multi-dimensional scaling approach to shape analysis, we develop bootstrap methods for inference about mean reflection shape and size-and-shape based on labelled landmark data. The approach is developed in general dimensions though we focus on the three-dimensional case. We consider two pivotal statistics which we use to construct bootstrap confidence regions for the mean reflection shape or size-and-shape, and present simulation results which show that these ...
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作者：Wang, Jianqiang C.; Opsomer, J. D.
作者单位：Colorado State University System; Colorado State University Fort Collins
摘要：Survey estimators of population quantities such as distribution functions and quantiles contain nondifferentiable functions of estimated quantities. The theoretical properties of such estimators are substantially more complicated to derive than those of differentiable estimators. In this article, we provide a unified framework for obtaining the asymptotic design-based properties of two common types of nondifferentiable estimators. Estimators of the first type have an explicit expression, while...
