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Title:

A Bayesian Semiparametric Latent Variable Model for Mixed Responses

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latent variable models, mixed responses, penalized splines, spatial effects, MCMC

latent variable models, mixed responses, penalized splines, spatial effects, MCMC Minimize

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article

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Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data

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Categorical time-space data, forest damage, latent utility models, Markov random fields, MCMC, probit models, semiparametric Bayesian inference, unemployment

Categorical time-space data, forest damage, latent utility models, Markov random fields, MCMC, probit models, semiparametric Bayesian inference, unemployment Minimize

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article

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A Mixed Model Approach for Geoadditive Hazard Regression

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Mixed model based approaches for semiparametric regression have gained much interest in recent years, both in theory and application. They provide a unified and modular framework for penalized likelihood and closely related empirical Bayes inference. In this article, we develop mixed model methodology for a broad class of Cox-type hazard regress...

Mixed model based approaches for semiparametric regression have gained much interest in recent years, both in theory and application. They provide a unified and modular framework for penalized likelihood and closely related empirical Bayes inference. In this article, we develop mixed model methodology for a broad class of Cox-type hazard regression models where the usual linear predictor is generalized to a geoadditive predictor incorporating non-parametric terms for the (log-)baseline hazard rate, time-varying coefficients and non-linear effects of continuous covariates, a spatial component, and additional cluster-specific frailties. Non-linear and time-varying effects are modelled through penalized splines, while spatial components are treated as correlated random effects following either a Markov random field or a stationary Gaussian random field prior. Generalizing existing mixed model methodology, inference is derived using penalized likelihood for regression coefficients and (approximate) marginal likelihood for smoothing parameters. In a simulation we study the performance of the proposed method, in particular comparing it with its fully Bayesian counterpart using Markov chain Monte Carlo methodology, and complement the results by some asymptotic considerations. As an application, we analyse leukaemia survival data from northwest England. Copyright 2007 Board of the Foundation of the Scandinavian Journal of Statistics. Minimize

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Bayesian inference for generalized additive mixed models based on Markov random field priors

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article

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Correction: Function estimation with locally adaptive dynamic models

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Some asymptotic results on generalized penalized spline smoothing

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The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be "a priori" normally distributed links the smoothi...

The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be "a priori" normally distributed links the smoothing framework to generalized linear mixed models. We consider the asymptotic rates such that the Laplace approximation is justified and the resulting fits in the mixed model correspond to penalized spline estimates. Secondly, we make use of a fully Bayesian viewpoint by imposing an "a priori" distribution on all parameters and coefficients. We argue that with the postulated rates at which the spline basis dimension increases with the sample size the posterior distribution of the spline coefficients is approximately normal. The validity of this result is investigated in finite samples by comparing Markov chain Monte Carlo results with their asymptotic approximation in a simulation study. Copyright (c) 2009 Royal Statistical Society. Minimize

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Bayesian varying-coefficient models using adaptive regression splines

Bayesian varying-coefficient models using adaptive regression splines Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-08-14

Source:

http://smj.sagepub.com/cgi/reprint/1/3/195.pdf

http://smj.sagepub.com/cgi/reprint/1/3/195.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Some asymptotic results on generalized penalized spline smoothing

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The paper discusses asymptotic properties of penalized spline smooth-ing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothin...

The paper discusses asymptotic properties of penalized spline smooth-ing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothing framework to generalized linear mixed models (GLMM). We consider the asymptotic rates such that Laplace approximation is justified and the resulting fits in the mixed model correspond to penalized spline estimates. Secondly, we make use of a full Bayesian viewpoint by imposing a priori distribution on all parameters and co-efficients. We argue that with the postulated rates at which the spline basis dimension increases with the sample size the posterior distribu-tion of the spline coefficients is approximately normal. The validity of this result is investigated in finite samples by comparing Markov Chain Monte Carlo (MCMC) results with their asymptotic approximation in a simulation study. 1 1 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2015-01-16

Source:

http://www.econ.kuleuven.ac.be/fetew/pdf_publicaties/KBI_0733.pdf

http://www.econ.kuleuven.ac.be/fetew/pdf_publicaties/KBI_0733.pdf Minimize

Document Type:

text

Language:

en

DDC:

519 Probabilities & applied mathematics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Regression Analysis of Forest Damage By Marginal Models for Correlated Ordinal Responses

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Studies on forest damage can generally not be carried out by common regression models, mainly for two reasons: Firstly, the response variable, damage state of trees, is usually observed in ordered categories. Secondly, responses are often correlated, either serially, as in a longitudinal study, or spatially, as in the application of this paper, ...

Studies on forest damage can generally not be carried out by common regression models, mainly for two reasons: Firstly, the response variable, damage state of trees, is usually observed in ordered categories. Secondly, responses are often correlated, either serially, as in a longitudinal study, or spatially, as in the application of this paper, where neighborhood interactions exist between damage states of spruces determined from aerial pictures. Thus so--called marginal regression models for ordinal responses, taking into account dependence among observations, are appropriate for correct inference. To this end we extend the binary models of Liang and Zeger (1986) and develop an ordinal GEE1 model, based on parametrizing association by global cross--ratios. The methods are applied to data from a survey conducted in Southern Germany. Due to the survey design, responses must be assumed to be spatially correlated. The results show that the proposed ordinal marginal regression models provi. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-12

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper09.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper09.ps.Z Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Projektpartner A geoadditive Bayesian Latent Variable Model for Poisson indicators

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We introduce a new latent variable model with count variable indicators, where usual linear parametric effects of covariates, nonparametric effects of continuous covariates and spatial effects on the continuous latent variables are modelled through a geoadditive predictor. Bayesian modelling of nonparametric functions and spatial effects is base...

We introduce a new latent variable model with count variable indicators, where usual linear parametric effects of covariates, nonparametric effects of continuous covariates and spatial effects on the continuous latent variables are modelled through a geoadditive predictor. Bayesian modelling of nonparametric functions and spatial effects is based on penalized spline and Markov random field priors. Full Bayesian inference is performed via an auxiliary variable Gibbs sampling technique, using a recent suggestion of Frühwirth-Schnatter and Wagner (2006). As an advantage, our Poisson indicator latent variable model can be combined with semiparametric latent variable models for mixed binary, ordinal and continuous indicator variables within an unified and coherent framework for modelling and inference. A simulation study investigates performance, and an application to post war human security in Cambodia illustrates the approach. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-20

Source:

http://epub.ub.uni-muenchen.de/1877/1/paper_508.pdf

http://epub.ub.uni-muenchen.de/1877/1/paper_508.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Latent variable models ; Poisson indicators ; penalized splines ; spatial effects

Latent variable models ; Poisson indicators ; penalized splines ; spatial effects Minimize

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