Shrinkage-based Variable Selection Methods for Linear Regression and Mixed-effects Models
In this dissertation we propose two new shrinkage-based variable selection approaches. We first propose a Bayesian selection technique for linear regression models, which allows for highly correlated predictors to enter or exit the model, simultaneously. The second variable selection method proposed is for linear mixed-effects models, where we develop a new technique to jointly select the important fixed and random effects parameters. We briefly summarize each of these methods below.literature, the problem of selecting variables/predictors has received considerable attention over the years, and a large ... regression, or regression shrinkage, has emerged as a highly-successful method to tackle this problem, for example see, anbsp;...
| Title | : | Shrinkage-based Variable Selection Methods for Linear Regression and Mixed-effects Models |
| Author | : | Arun Krishna |
| Publisher | : | ProQuest - 2009 |
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