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Bayesian Inference of a Non normal Multivariate Partial Linear Regression Model
2021
Iraqi journal of statistical sciences
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This research includes the Bayesian estimation of the parameters of the multivariate partial linear regression model when the random error follows the matrix-variate generalized modified Bessel distribution ...
of the shape parameters, the kernel function used in the generation was a Gaussian kernel function, the bandwidth (Smoothing) parameter was according to the rule of thumb. ...
with a generalized inverse Gaussian distribution as well as its applications in the Bayesian analysis of the normal multiple linear regression model assuming a generalized inverse Gaussian distribution ...
doi:10.33899/iqjoss.2021.169967
fatcat:kfqj6hxzgvewvfxho6auqjzhtu
Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models
2007
Biometrics
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Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. ...
gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). ...
One can easily see that under this Bayesian model, the semiparametric model (1) becomes the linear mixed model representation (11). ...
doi:10.1111/j.1541-0420.2007.00799.x
pmid:18078480
pmcid:PMC2665800
fatcat:p3wjf34fazb4nn4p2e5pt7lcfu
bWGR: Bayesian Whole-Genome Regression
2019
Bioinformatics
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The package also enables fitting efficient multivariate models and complex hierarchical models. ...
Here we introduce bWGR, an R package that enables users to efficient fit and cross-validate Bayesian and likelihood whole-genome regression methods. ...
The generalized function 'wgr' enable users to combine a WGR with a kernel method, such as combining BayesB and RKHS. ...
doi:10.1093/bioinformatics/btz794
pmid:31647543
fatcat:tvp4tcavnfdtzelhp5ssll2zzq
Bayesian semiparametric regression models for evaluating pathway effects on continuous and binary clinical outcomes
2012
Statistics in Medicine
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It can incorporate prior knowledge into Bayesian hierarchical model formulation and makes inference by using the posterior samples without asymptotic theory. ...
We compare our Bayesian approaches with a likelihood-based approach that was developed by relating a least squares kernel machine for nonparametric pathway effect with a restricted maximum likelihood for ...
: • a semiparametric mixed model based on all genes except the gth gene, (4) • a semiparametric mixed model based on all genes except the gth and g′th genes, (5) For the Bayesian approach, we use the ...
doi:10.1002/sim.4493
pmid:22438129
pmcid:PMC3763871
fatcat:mrpg5hn7pzdrjlcbsbypkvpnnu
BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models
2018
G3: Genes, Genomes, Genetics
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Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. ...
In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. ...
Bayesian linear mixed models The BGGE solves the linear mixed models through Bayesian hierarchical modeling. ...
doi:10.1534/g3.118.200435
pmid:30049744
pmcid:PMC6118304
fatcat:22s7k7nxaffvpjxe2vrhmiubq4
Bayesian bandwidth estimation for a functional nonparametric regression model with mixed types of regressors and unknown error density
2014
Journal of nonparametric statistics (Print)
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2014 pre-print arXiv:1403.1913v1 fatcat:bokqolcxcfgkjfvfddwkooxdf4
Illustrated by a spectroscopy data set in the food quality control, we applied the proposed Bayesian approach to select the optimal bandwidths in a nonparametric functional regression model with mixed ...
We derive a kernel likelihood and posterior density for the bandwidth parameters under the kernel-form error density, and put forward a Bayesian bandwidth estimation approach that can simultaneously estimate ...
In this paper, we extend this Bayesian approach to the same nonparametric functional regression model with mixed types of regressors. ...
doi:10.1080/10485252.2014.916806
fatcat:6owjdtf7qrboheo3zl2d4dkmne
Uncertainties here, there, and everywhere: interpolating between small- and large-g expansions using Bayesian Model Mixing
[article]
2022
arXiv
pre-print
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Bayesian Model Mixing (BMM) is a statistical technique that can be used to combine models that are predictive in different input domains into a composite distribution that has improved predictive power ...
We consider three mixing methods: linear mixture BMM, localized bivariate BMM, and localized multivariate BMM with Gaussian processes. ...
LOCALIZED BIVARIATE BAYESIAN MODEL MIXING A. ...
arXiv:2206.04116v1
fatcat:yvzazfn3jbgazjaizzccz25bu4
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
[article]
2024
arXiv
pre-print
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Using adapted covariance models and dedicated enrichment strategy for the Gaussian processes in Bayesian optimization, this approach allows to reduce the computational cost up to two orders of magnitude ...
Existing Bayesian Quality-Diversity approaches suited for intensive high-fidelity simulations are not adapted to mixed variables constrained optimization problems. ...
In Section 3, the proposed Bayesian QD approach and the general algorithm are presented, with two versions depending on the covariance model used in the Gaussian Process. ...
arXiv:2310.05955v3
fatcat:ewhtga245jcx5ckf6qo6ime6yy
Bayesian nonparametric reliability analysis for a railway system at component level
2013
2013 IEEE International Conference on Intelligent Rail Transportation Proceedings
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This paper proposes a nonparametric Bayesian approach with a Dirichlet Process Mixture Model (DPMM) to facilitate reliability analysis in a railway system. ...
A Bayesian nonparametric model using mixtures of Weibull distributions is developed in a previous study [11] . This is mixing on both the shape and scale parameters of the Weibull kernel. ...
selection especially for data of mixed types. ...
doi:10.1109/icirt.2013.6696293
fatcat:ieun2q3rvjcdhchwptclzlok2y
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 ...
None of these use mixed models or hierarchical Bayesian approaches. ...
The authors use a simulation experiment to compare the reproducing kernel mixed model method with a parametric mixed model approach. ...
doi:10.1214/09-ejs525
pmid:20305800
pmcid:PMC2841361
fatcat:37rhsccmvzeh3kiyuopffbw43q
Bayesian nonnegative Matrix Factorization with volume prior for unmixing of hyperspectral images
2009
2009 IEEE International Workshop on Machine Learning for Signal Processing
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kernel with a corresponding pre-processed spectrum from 950 -1650nm. ...
Non-linear mixing are usually neglected due to minor significance and increased modeling complexity [2] . ...
Based on these endmembers, 100 samples were generated with full mixing profile and a noise variance (72 = 10-3 . This leads to a data matrix X of 3 x 100 illustrated in figure 5 . ...
doi:10.1109/mlsp.2009.5306262
fatcat:45bldh6lwrcx7cgl5pal4wh5yu
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
2016
Econometrics
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Article Bayesian bandwidth selection for a nonparametric regression model with mixed types of regressors Econometrics Provided in Cooperation with: MDPI -Multidisciplinary Digital Publishing Institute, ...
Basel Suggested Citation: Zhang, Xibin; King, Maxwell L.; Shang, Han Lin (2016) : Bayesian bandwidth selection for a nonparametric regression model with mixed types of regressors, Standard-Nutzungsbedingungen ...
In many empirical applications of nonparametric regression models, regressors are often of mixed types such as continuous and categorical. ...
doi:10.3390/econometrics4020024
fatcat:isuox3ujzfgrtksn6a3pkw4hpi
Mode jumping MCMC for Bayesian variable selection in GLMM
2018
Computational Statistics & Data Analysis
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Generalized linear mixed models (GLMM) are used for inference and prediction in a wide range of different applications providing a powerful scientific tool. ...
In a Bayesian setting, the posterior distribution of the models, based on the observed data, can be viewed as a relevant measure for the model evidence. ...
Introduction In this paper we study variable selection in generalized linear mixed models (GLMM) addressed in the Bayesian setting. ...
doi:10.1016/j.csda.2018.05.020
fatcat:f5xv7sjmanf43fps6bxkn2kwsm
Adaptive-modal Bayesian nonparametric regression
2012
Electronic Journal of Statistics
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We introduce a novel, Bayesian nonparametric, infinite-mixture regression model. ...
The model has unimodal kernel (component) densities, and has covariate-dependent mixture weights that are defined by an infinite ordered-category probits regression. ...
Nearly all Bayesian models that were parameterized by a vector of fixed (non-mixed) regression coefficients β (e.g., β = (β 1 , . . . , β p ) ⊺ for the DP-mixed intercepts regression model) assigned a ...
doi:10.1214/12-ejs738
fatcat:nw47hux33zdmnkhleqisia62hu
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2305.13998v2 fatcat:j3hberszy5fkbgyezgbdxdqrtyOther Versions
This release adds the capability to handle mixed-variable surrogate models and hierarchical variables. ...
SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. ...
KerGP [70] (developed in R) implements more general kernels but there is no Python open-source toolbox that implements more general kernels to deal with mixed variables, such as the homoscedastic hypersphere ...
arXiv:2305.13998v3
fatcat:emntzuy6lnecfo3g4ylqxhaisy
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