A new model selection procedure based on dynamic quantile regression

2014 ◽  
Vol 41 (10) ◽  
pp. 2240-2256
Author(s):  
Wei Xiong ◽  
Maozai Tian
Keyword(s):  
Model Selection ◽  
Quantile Regression ◽  
Selection Procedure ◽  
New Model ◽  
Model Selection Procedure

scholarly journals The Lasso as an ℓ1-ball model selection procedure

2011 ◽  
Vol 5 (0) ◽  
pp. 669-687 ◽  
Author(s):  
Pascal Massart ◽  
Caroline Meynet
Keyword(s):  
Model Selection ◽  
Selection Procedure ◽  
Model Selection Procedure

scholarly journals Prior hypotheses or regularization allow inference of diversification histories from extant timetrees

2020 ◽  
Author(s):  
Hélène Morlon ◽  
Florian Hartig ◽  
Stéphane Robin
Keyword(s):  
Model Selection ◽  
Infinite Number ◽  
Selection Procedure ◽  
Data Driven ◽  
Model Selection Procedure ◽  
Extinction Rates ◽  
Extant Species ◽  
Over Time

AbstractPhylogenies of extant species are widely used to study past diversification dynamics1. The most common approach is to formulate a set of candidate models representing evolutionary hypotheses for how and why speciation and extinction rates in a clade changed over time, and compare those models through their probability to have generated the corresponding empirical tree. Recently, Louca & Pennell2 reported the existence of an infinite number of ‘congruent’ models with potentially markedly different diversification dynamics, but equal likelihood, for any empirical tree (see also Lambert & Stadler3). Here we explore the implications of these results, and conclude that they neither undermine the hypothesis-driven model selection procedure widely used in the field nor show that speciation and extinction dynamics cannot be investigated from extant timetrees using a data-driven procedure.

scholarly journals Bayesian analysis of fMRI data and RNA-Seq time course experiment data

2015 ◽  
Author(s):  
◽  
Yuan Cheng
Keyword(s):  
Model Selection ◽  
Bayesian Analysis ◽  
Differentially Expressed Genes ◽  
Time Course ◽  
Selection Procedure ◽  
Principal Component ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Hemodynamic Response Function ◽  
Model Selection Procedure

The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to describe the initial dip. We use HRF to inform voxel-wise neuronal activities. Then we devise a new model selection procedure with a nonlocal pMOM prior for joint detection of neuronal activation and estimation of HRF, in order to time the activation time difference between visual and motor areas in the brain. In the second part, we develop a new Bayesian analysis of RNA-Seq Time Course experiments data. We propose to use Bayesian Principal Component regression model and based on that, devise a model selection procedure by using nonlocal piMOM prior in order to identify differentially expressed genes. Most current existing methods for RNA-Seq Time Course experiments data are from static view of point and cannot predict temporal patterns. Our method estimate the posterior differentially expressed probability for each gene by borrowing information across all subjects. Use of nonlocal prior in the model selection procedure reduces false discovered differentially expressed genes.

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