Sparse Bayesian Variable Selection in Classification Problems with Large p Small n

論文翻譯標題: 稀疏變數選取應用於分類分析中變數個數大於樣本數
  • 陳 彥龍

學生論文: Master's Thesis

摘要

Finite mixture of regression models provide a flexible method of modeling data that arise from a heterogeneous population Within each sub-population the response variable can be explained by a linear regression on the predictor variables If the number of predictor variables is large it is assumed that only a small subset of variables are important for explaining the response variable It is further assumed that for different subpopulations different subsets of variables may be needed to explain the response variable An Reversible jump MCMC method is proposed to the variable selection procedure that accounts not only for identifying the important variables in each subpopulation but also for determining the number of components in the finite mixture of regression models
獎項日期2016 七月 7
原文English
監督員Kuo-Jung Lee (Supervisor)

引用此

Sparse Bayesian Variable Selection in Classification Problems with Large p Small n
彥龍, 陳. (Author). 2016 七月 7

學生論文: Master's Thesis