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Bayesian Semiparametric Inference for the Accelerated Failure Time Model

• Authors : Lynn Kuo; Bani Mallick; The Pennsylvania State University CiteSeerX Archives

Subjects: Key words and phrases; Censored data; Log linear model

• Source: ftp://merlot.stat.uconn.edu/pub/papers/tr95/tr9537.ps

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Bayes factor asymptotics for variable selection in the Gaussian process framework.

• Authors : Mukhopadhyay, Minerva¹ (AUTHOR) ; Bhattacharya, Sourabh² (AUTHOR)

• Source: Annals of the Institute of Statistical Mathematics. Jun2022, Vol. 74 Issue 3, p581-613. 33p.

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Multivariate Gaussian RBF‐net for smooth function estimation and variable selection.

• Source: Statistical Analysis & Data Mining. Oct2021, Vol. 14 Issue 5, p484-500. 17p.

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An integrative Bayesian Dirichletmultinomial regression model for the analysis of taxonomic abundances in microbiome data.

• Authors : Wadsworth, W. Duncan¹; Argiento, Raffaele²; Guindani, Michele³

• Source: BMC Bioinformatics. 2/8/2017, Vol. 18, p1-12. 12p. 3 Diagrams, 3 Charts, 3 Graphs.

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Bayesian inference for high-dimensional linear regression under mnet priors.

• Authors : Tan, Aixin¹; Huang, Jian¹

• Source: Canadian Journal of Statistics. Jun2016, Vol. 44 Issue 2, p180-197. 18p.

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Random Effects Selection in Linear Mixed Models.

• Authors : Chen, Zhen¹ ; Dunson, David B.²

• Source: Biometrics. Dec2003, Vol. 59 Issue 4, p762-769. 8p. 3 Charts, 1 Graph.

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Bayesian Variable Selection for Gaussian copula regression models.

• Authors : Alexopoulos, A; Bottolo, L

Subjects: Gaussian copula; Mixed data; Multiple-response regression models

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Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

• Authors : Naveau, Marion; Kon Kam King, Guillaume; Rincent, Renaud ...

Subjects: High-dimension; Non-linear mixed-effects models; SAEM algorithm

• Source: https://hal.inrae.fr/hal-03685060 ; 2023.

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High-Dimensional Inference and Uncertainty Quantification for Variable Selection, Clustering and Object-oriented Analysis with Bayesian and Approximate Bayesian Methods

• Authors : Gutierrez, Rene

Subjects: Statistics; Approximate Methods; Bayesian

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An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data

• Authors : Wadsworth, W. Duncan; Argiento, Raffaele; Guindani, Michele

Subjects: Bayesian hierarchical model; Data integration; Dirichlet-multinomial

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