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11
Semiparametric Regression During 2003–2007
, 2008
"... Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a ..."
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Cited by 17 (5 self)
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Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Bayesian semiparametric structural equation models with latent variables
 Psychometrika
, 2010
"... Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions ..."
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Cited by 5 (2 self)
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Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semiparametric Bayesian SEMs, which allow mixed categorical and continuous manifest variables while also allowing the latent variables to have unknown distributions. In order to include typical identifiability restrictions on the latent variable distributions, we rely on centered Dirichlet process (CDP) and CDP mixture (CDPM) models. The CDP will induce a latent class model with an unknown number of classes, while the CDPM will induce a latent trait model with unknown densities for the latent traits. A simple and efficient Markov chain Monte Carlo algorithm is developed for posterior computation, and the methods are illustrated using simulated examples, and several applications.
Geoadditive Latent Variable Modelling of Count Data on Multiple Sexual Partnering in Nigeria
"... that multiple sexual partnering increases the risk of contracting HIV and other sexually transmitted diseases. Therefore, partner reduction is one of the prevention strategies to accomplish the Millenium development goal of halting and reversing the spread of HIV/AIDS. In order to explore possible a ..."
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Cited by 2 (2 self)
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that multiple sexual partnering increases the risk of contracting HIV and other sexually transmitted diseases. Therefore, partner reduction is one of the prevention strategies to accomplish the Millenium development goal of halting and reversing the spread of HIV/AIDS. In order to explore possible association between sexual partnering and some risk factors, this paper utilizes a novel Bayesian geoadditive latent variable model for count outcomes. This allows us to simultaneously analyze linear and nonlinear effects of covariates as well as spatial variations of one or more latent variables, such as attitude towards multiple partnering, which in turn directly influences the multivariate observable outcomes or indicators. Influence of demographic factors such as age, gender, locality, state of residence, educational attainment, etc., and knowledge about HIV/AIDS on attitude towards multiple partnering is also investigated. Results can provide insights to policy makers with the aim of reducing the spread of HIV and AIDS among the Nigerian populace through partner reduction. 1 Key words: factor loading; geographical variations; latent variable model; MCMC; Nigeria; semiparametric Poisson model; 1.
IrregularTime Bayesian Networks
"... In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discretetime Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such proces ..."
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Cited by 2 (1 self)
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In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discretetime Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuoustime Markov models assume either a discrete state space (as ContinuousTime Bayesian Networks), or a flat continuous state space (as stochastic differential equations). To address these problems, we present a new modeling class called IrregularTime Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced timepoints, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of the available data. 1
Projektpartner A geoadditive Bayesian Latent Variable Model for Poisson indicators
, 2006
"... 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 nonp ..."
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Cited by 1 (1 self)
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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 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 FrühwirthSchnatter 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.
Bayesian Modeling for Multivariate Mixed Outcomes with Applications to Cognitive Testing Data
, 2012
"... Statistics And Social Work This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and modelbased clustering for mixed outcomes. We use the term mixed outcomes to refer to binary, ordered categorical, count, continuous and other ordered ..."
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Statistics And Social Work This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and modelbased clustering for mixed outcomes. We use the term mixed outcomes to refer to binary, ordered categorical, count, continuous and other ordered outcomes in combination. Such data structures are common in social, behavioral, and medical sciences. We first review existing parametric approaches to mixed outcomes in latent variable models before developing extensions to accommodate outcome types specific to cognitive testing data. We subsequently develop two new regression approaches for mixed outcome data, the semiparametric Bayesian latent variable model and the semiparametric reduced rank multivariate regression model. In contrast to the existing parametric approaches, these models allow us to avoid specification of distributions for each outcome type. We apply the latent variable and multivariate regression models to investigate the association between cognitive outcomes and MRImeasured regional brain volumes using data from a study of dementia and compare results from the different models. Finally, we develop a new semiparametric correlated partial membership model for modelbased clustering of
Bayesian Linear Regression — Different Conjugate Models and Their (In)Sensitivity to PriorData Conflict
"... The paper is concerned with Bayesian analysis under priordata conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior). Two approaches for Bayesian linear regression modeling based on conj ..."
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The paper is concerned with Bayesian analysis under priordata conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior). Two approaches for Bayesian linear regression modeling based on conjugate priors are considered in detail, namely the standard approach also described in Fahrmeir, Kneib & Lang (2007) and an alternative adoption of the general construction procedure for exponential family sampling models. We recognize that – in contrast to some standard i.i.d. models like the scaled normal model and the BetaBinomial / DirichletMultinomial model, where priordata conflict is completely ignored – the models may show some reaction to priordata conflict, however in a rather unspecific way. Finally we briefly sketch the extension to a corresponding imprecise probability model, where, by considering sets of prior distributions instead of a single prior, priordata conflict can be handled in a very appealing and intuitive way. Key words: Linear regression; conjugate analysis; priordata conflict; imprecise probability 1
Labour Market Integration of Immigrants and their Children in Germany: Does Personality Matter?
, 2010
"... Educational attainment, length of stay, di¤erences in national background and language skills play an acknowledged important role for the integration of immigrants. But integration is also a social process, which suggests that psychological factors are relevant. This paper explores whether and to wh ..."
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Educational attainment, length of stay, di¤erences in national background and language skills play an acknowledged important role for the integration of immigrants. But integration is also a social process, which suggests that psychological factors are relevant. This paper explores whether and to what extent immigrants and their children need to believe in their ability to control their own success, in other words their sense of control. To quantify this personal trait I use a measure of an individual’s sense of control over outcomes in life such as …nding a job. This measure is known in psychology as "the locus of control". I …rst estimate an exogenous measure. Then I address the problem that this measure is actually endogeneous in a labor market outcome equation by employing a model in which the sense of control is an endogenized latent factor in a simultaneous equation model. The determinants of this sense of control as well as its e¤ect on the probability of being employed are examined. The model is estimated using an implemented Bayesian Markov Chain Monte Carlo algorithm. Results with endogenized personality indicate that, on average, immigrants believe less than natives in being able to control outcomes in life, but children of immigrants have already a stronger sense of control than their parents. The paper also …nds that sense of control over life’s outcomes positively contributes to the probability of being employed. This means that immigrants and their children face a double disadvantage on the labor market: they are disadvantaged because of their status as an immigrant and they have a lower sense of being able to control their situation, which is a personality trait that matters on the labour market. 1
XML Template (2014) [7.2.2014–1:41pm] [1–22] //blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/SMMJ/Vol00000/140014/APPFile/SGSMMJ140014.3d (SMM) [PREPRINTER stage] Article
"... Bayesian analysis of transformation latent variable models with multivariate censored data ..."
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Bayesian analysis of transformation latent variable models with multivariate censored data