scholarly journals Generalized ridge estimator and model selection criteria in multivariate linear regression

2018 ◽  
Vol 165 ◽  
pp. 243-261
Author(s):  
Yuichi Mori ◽  
Taiji Suzuki
Keyword(s):  
Model Selection ◽  
Linear Regression ◽  
Selection Criteria ◽  
Multivariate Linear Regression ◽  
Ridge Estimator ◽  
Model Selection Criteria ◽  
Generalized Ridge Estimator

scholarly journals Marker Pair Selection for Mapping Quantitative Trait Loci

Genetics ◽  
2001 ◽  
Vol 157 (1) ◽  
pp. 433-444 ◽  
Author(s):  
Hans-Peter Piepho ◽  
Hugh G Gauch
Keyword(s):  
Quantitative Trait Loci ◽  
Model Selection ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quantitative Trait ◽  
Selection Criteria ◽  
Marker Pair ◽  
Regression Problem ◽  
Model Selection Criteria ◽  
Trait Loci

AbstractMapping of quantitative trait loci (QTL) for backcross and F2 populations may be set up as a multiple linear regression problem, where marker types are the regressor variables. It has been shown previously that flanking markers absorb all information on isolated QTL. Therefore, selection of pairs of markers flanking QTL is useful as a direct approach to QTL detection. Alternatively, selected pairs of flanking markers can be used as cofactors in composite interval mapping (CIM). Overfitting is a serious problem, especially if the number of regressor variables is large. We suggest a procedure denoted as marker pair selection (MPS) that uses model selection criteria for multiple linear regression. Markers enter the model in pairs, which reduces the number of models to be considered, thus alleviating the problem of overfitting and increasing the chances of detecting QTL. MPS entails an exhaustive search per chromosome to maximize the chance of finding the best-fitting models. A simulation study is conducted to study the merits of different model selection criteria for MPS. On the basis of our results, we recommend the Schwarz Bayesian criterion (SBC) for use in practice.

scholarly journals Generalized Measures of Divergence in Survival Analysis and Reliability

2010 ◽  
Vol 47 (1) ◽  
pp. 216-234 ◽  
Author(s):  
Filia Vonta ◽  
Alex Karagrigoriou
Keyword(s):  
Survival Analysis ◽  
Model Selection ◽  
Mutual Information ◽  
Selection Criteria ◽  
Cox Model ◽  
Transformation Model ◽  
Model Selection Criteria ◽  
Divergence Measures

Measures of divergence or discrepancy are used either to measure mutual information concerning two variables or to construct model selection criteria. In this paper we focus on divergence measures that are based on a class of measures known as Csiszár's divergence measures. In particular, we propose a measure of divergence between residual lives of two items that have both survived up to some time t as well as a measure of divergence between past lives, both based on Csiszár's class of measures. Furthermore, we derive properties of these measures and provide examples based on the Cox model and frailty or transformation model.

scholarly journals PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION

2015 ◽  
Vol 28 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Shuichi Kawano ◽  
Ibuki Hoshina ◽  
Kaito Shimamura ◽  
Sadanori Konishi
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Selection Criteria ◽  
Lasso Regression ◽  
Bayesian Lasso ◽  
Model Selection Criteria
Sign in / Sign up

Export Citation Format

Share Document