The Asymptotic Properties of Automatically Selected Tuning Parameter in the Minimum Density Power Divergence Estimator

• 발행기관: 한국자료분석학회
• 저자명: 김재선(Jaeseon Kim) 홍창곤(Changkon Hong)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.18 No.6, 2879~2887쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2016.12.30

영문 초록

A family of density-based divergence measures called density power divergences was suggested by Basu et al. (1998). This is a family of measures indexed by single tuning parameter alpha. The tuning parameter controls the trade-off between robustness and asymptotic efficiency of the estimators. This family includes the Kullback-Leibler divergence (Kullback, Leibler, 1951) and L_2-distance. With a suitably chosen tuning parameter, a minimum density power divergence estimator (MDPDE) can be obtained. For 0

목차

1. Introduction

2. The minimum density power divergence estimator and a criterion for α-selection

3. The asymptotic optimality of the MDPDE with automatically selected tuning parameter

4. Concluding Remarks

References

키워드

Density power divergence Density-based minimum divergence method Robustness Tuning parameter Asymptotic optimality

참고문헌