Bayesian Analysis of Skewed Regression Model in the “Large P, Small N” Paradigm

• 발행기관: 한국자료분석학회
• 저자명: 정윤식(Younshik Chung) 박정금(Junggeum Park)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.8 No.5, 1705~1713쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2006.10.30

영문 초록

This paper performed the skewed regression model whose data set of n observed responses is to be related to a set of p potential predictors with p >> n. We introduce the skewed regression models based on the ideas proposed by Chen, Dey and Shao(1999). We use singular-value decompositions of matrices which have measured values of large numbers of predictors across samples, generating factor representations and possibly massive dimension reduction. Also, we develop easily implemented and standard Markov chain Monte Carlo(MCMC) methods to produce posterior inferences on the high-dimensional regression parameter. Finally, we have the simulation study.

목차

1. INTRODUCTION

2. PRELIMINARIES

3. SKEWED REGRESSION MODEL

4. SIMULATION STUDIES

REFERENCES

키워드

Bayesian analysis Generalized singular g-prior Singular-value decompositions(SVD) Skewed regression models Markov chain Monte Carlo(MCMC)