Results 11 to 20 of about 4,032,520 (72)

Prior elicitation and variable selection for bayesian quantile regression [PDF]

open access: yes, 2013
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
core   +7 more sources

Regularized and robust regression methods for high dimensional data [PDF]

open access: yes, 2014
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Recently, variable selection in high-dimensional data has attracted much research interest.
Hashem, Hussein Abdulahman
core   +7 more sources

A partially collapsed Gibbs sampler for Bayesian quantile regression [PDF]

open access: yes, 2009
We introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler.
Yu, K, Reed, C
core   +6 more sources

Who Knows Best What the Next Year Will Hold for You? The Validity of Direct and Personality‐based Predictions of Future Life Experiences Across Different Perceivers

open access: yesEuropean Journal of Personality, EarlyView., 2020
Abstract This study explored the validity of person judgements by targets and their acquaintances (‘informants’) in longitudinally predicting a broad range of psychologically meaningful life experiences. Judgements were gathered from four sources (targets, N = 189; and three types of informants, N = 1352), and their relative predictive validity was ...
Nele M. Wessels   +3 more
wiley   +1 more source

Penalized flexible Bayesian quantile regression [PDF]

open access: yes, 2012
Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can
Yu, K, Alkenani, A, Alhamzawi, R
core   +1 more source

Penalized single-index quantile regression [PDF]

open access: yes, 2013
This article is made available through the Brunel Open Access Publishing Fund. Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
Yu, K
core   +1 more source

Some statistical methods for dimension reduction [PDF]

open access: yes, 2013
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe aim of the work in this thesis is to carry out dimension reduction (DR) for high dimensional (HD) data by using statistical methods for variable ...
Al-Kenani, Ali J Kadhim
core   +4 more sources

Power prior elicitation in Bayesian quantile regression [PDF]

open access: yes, 2011
This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2011 Rahim Alhamzawi and Keming Yu.We address a quantile dependent prior for Bayesian quantile regression.
Rahim Alhamzawi   +3 more
core   +1 more source

Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]

open access: yes, 2008
During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Konrath, Susanne   +2 more
core   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

open access: yesAIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin   +2 more
wiley   +1 more source

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