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A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models
2013
Psychometrika
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Keywords Bayesian P-splines; latent variables; MCMC methods; semiparametric models Requests for reprints should be sent to Jing-Heng Cai, ...
The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. ...
The Bayesian P-splines approach, on which the semiparametric SEM is based, is described in this section as well. ...
doi:10.1007/s11336-013-9323-7
pmid:24092481
pmcid:PMC5129644
fatcat:pgdj46h5kbcu3dptonjwkeshem
Semiparametric Smoothing Spline in Joint Mean and Dispersion Models with Responses from the Biparametric Exponential Family: A Bayesian Perspective
2021
Statistics, Optimization and Information Computing
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We rely on the natural connection among smoothing methods that use basis functions with penalization, mixed models and a Bayesian Markov Chain sampling simulation methodology. ...
This article extends the fusion among various statistical methods to estimate the mean and variance functions in heteroscedastic semiparametric models when the response variable comes from a two-parameter ...
An attractive advantage of penalized splines compared to smoothing splines is the ease at which MCMC schemes fit semiparametric models with reduced basis functions. ...
doi:10.19139/soic-2310-5070-671
fatcat:wp67s4be3zfyri5jrpo3imlqqy
Recent Advances in Semiparametric Bayesian Function Estimation
[chapter]
1999
Mathematische Methoden der Wirtschaftswissenschaften
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This paper surveys recent developments in semiparametric Bayesian inference for generalized regression and outlines some directions in current research. ...
Common nonparametric curve tting methods such as spline smoothing, local polynomial regression and basis function approaches are now w ell developed and widely applied. ...
For the case of several regressors, generalized additive and semiparametric additive models are de ned by extending ( 14 ) to i = h( + p X j=1 f (x ij ) + w 0 i ): (15) For semiparametric Bayesian inference ...
doi:10.1007/978-3-662-12433-8_12
fatcat:y6l37wv7uzh2tdxsgwcl2s3btu
Semiparametric regression during 2003–2007
2009
Electronic Journal of Statistics
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Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology -thus allowing ...
We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. ...
With robustness in mind, Jullion & Lambert [139] study prior specification for Bayesian P-splines models. ...
doi:10.1214/09-ejs525
pmid:20305800
pmcid:PMC2841361
fatcat:37rhsccmvzeh3kiyuopffbw43q
A Computational Bayesian Method for Estimating the Number of Knots In Regression Splines
2011
Bayesian Analysis
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A semiparametric model can be expressed as a set of penalized regression splines, and, more generally, as a linear mixed model. ...
A new Gibbs sampler is developed here for the number and positions of knots in regression splines by expressing semiparametric regression as a linear mixed model with a random effect term for the basis ...
The convergence properties of the proposed Bayesian semiparametric regression are discussed based on the Halpern's Bayesian spline model setting with conjugate priors (Halpern 1973) . ...
doi:10.1214/11-ba629
fatcat:vtjur52vj5d4lftesmwvvll3qq
Semiparametric Regression Analysis via Infer.NET
2018
Journal of Statistical Software
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We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in graphical models. ...
Potentially, this contribution represents the start of a new era for semiparametric regression, where large and complex analyses are performed via fast graphical models methodology and software, mainly ...
Semiparametric mixed model Since semiparametric regression models based on penalized splines fit in the mixed model framework, semiparametric longitudinal data analysis can be performed by fusion with ...
doi:10.18637/jss.v087.i02
fatcat:udeublxi7bfybk5h3w24qa223q
The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis
2011
Transportation Research Part B: Methodological
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Transportation Research Part B: Methodological, 45 (3), pp. p.461 -473 ABSTRACT The multinomial logit model in discrete choice analysis is widely used in transport research. ...
The estimation of the semiparametric choice model is also investigated and empirical studies are used to illustrate the developed method. ...
The Bayesian P-splines approach The P-splines approach was developed by Eilers and Marx (1996) . ...
doi:10.1016/j.trb.2010.09.007
fatcat:a3uxtx74snbqrjtxxu43bk3tmm
Density Estimation via Bayesian Inference Engines
[article]
2021
arXiv
pre-print
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We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation ...
Moreover, the approach is fully Bayesian and all estimates are accompanied by pointwise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates. ...
Bayesian Poisson nonparametric regression using mixed model representations of low-rank smoothing splines (e.g. ...
arXiv:2009.06182v4
fatcat:w4mj4vlvnvcatkitp4sbkm5sfu
Semiparametric Regression for Periodic Longitudinal Hormone Data from Multiple Menstrual Cycles
2000
Biometrics
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The within-subject correlation is modeled using subject-specific random effects and a random stochastic process with a periodic variance function. ...
We consider semiparametric regression for periodic longitudinal data. ...
It follows that the periodic semiparametric mixed model ( 2 ) can be written as a modified linear mixed model, Y = 16 + Xp +NBa + Zb + U + E , where 1 is an n x 1 vector of ones, pr = (6, p')T are regression ...
doi:10.1111/j.0006-341x.2000.00031.x
pmid:10783774
fatcat:cz32upewb5capir4niu3qy7whq
On semiparametric regression with O'Sullivan penalised splines
[article]
2007
arXiv
pre-print
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This is an expos\'e on the use of O'Sullivan penalised splines in contemporary semiparametric regression, including mixed model and Bayesian formulations. ...
O'Sullivan penalised splines are similar to P-splines, but have an advantage of being a direct generalisation of smoothing splines. Exact expressions for the O'Sullivan penalty matrix are obtained. ...
Carlo (MCMC) schemes for fitting Bayesian semiparametric regression models -due to the reduction in the number of basis functions. ...
arXiv:0707.0143v1
fatcat:gtanyajqqjesvfbknlsyiiziam
ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALISED SPLINES
2010
Australian & New Zealand journal of statistics (Print)
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This is an exposé on the use of O'Sullivan penalised splines in contemporary semiparametric regression, including mixed model and Bayesian formulations. ...
O'Sullivan penalised splines are similar to P-splines, but have an advantage of being a direct generalisation of smoothing splines. Exact expressions for the O'Sullivan penalty matrix are obtained. ...
Carlo (MCMC) schemes for fitting Bayesian semiparametric regression models -due to the reduction in the number of basis functions. ...
doi:10.1111/j.1467-842x.2010.00578.x
fatcat:7wirscewkfbs7fvvztgcxsuxja
ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES
2008
Australian & New Zealand journal of statistics (Print)
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An exposition on the use of O'Sullivan penalized splines in contemporary semiparametric regression, including mixed model and Bayesian formulations, is presented. ...
O'Sullivan penalized splines are similar to P-splines, but have the advantage of being a direct generalization of smoothing splines. Exact expressions for the O'Sullivan penalty matrix are obtained. ...
Carlo (MCMC) schemes for fitting Bayesian semiparametric regression models -as a result of the reduction in the number of basis functions. ...
doi:10.1111/j.1467-842x.2008.00507.x
fatcat:zfrqalhvcjfhdna5hukvesgtiq
Issues in Claims Reserving and Credibility: A Semiparametric Approach With Mixed Models
2008
Journal of Risk and Insurance
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Using the statistical methodology of semiparametric regression and its connection with mixed models (see e.g. ...
Firstly, a Bayesian implementation of these smoothing models is relatively straightforward and allows simulation from the full predictive distribution of quantities of interest. ...
The semiparametric models in this paper are implemented via the concept of penalized regression splines (also called P-splines or pseudo-splines) and their connection with mixed models (as discussed for ...
doi:10.1111/j.1539-6975.2008.00278.x
fatcat:227grlqoqzfmzbq5tpsy2p6b3u
Semiparametric regression models for spatial prediction and uncertainty quantification of soil attributes
2016
Stochastic environmental research and risk assessment (Print)
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Bayesian semiparametric models yielded the best predictive results and provided empirical coverage probability nearly equal to nominal. ...
The semiparametric models outperformed competing models in all cases. ...
Semiparametric regression modeling can be accomplished with spline and radial basis functions (Buhmann 2003; Bliznyuk et al. 2008 Bliznyuk et al. , 2012 , which provide a flexible and possibly nonlinear ...
doi:10.1007/s00477-016-1337-0
fatcat:4h3q4u6klzgjll7r66rci3amny
Semiparametric Regression in Capture-Recapture Modeling
2006
Biometrics
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We propose nonparametric and semiparametric regression methods for estimating survival in capture-recapture models. ...
A fully Bayesian approach using Markov chain Monte Carlo simulations was employed to estimate the model parameters. ...
., 2005 for implementation examples of nonparametric Bayesian P-splines in WinBUGS). ...
doi:10.1111/j.1541-0420.2005.00514.x
pmid:16984309
fatcat:wba4d6icsjfpddlhfyi5fch774
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