A Robust Estimation for the Composite Lognormal-Pareto Model
자연과학 > 통계학
( Ro Jin Pak )
CSAM(Communications for Statistical Applications and Methods) 2013년, 제20권 제4호, 311~319페이지(총9페이지)
02706321.pdf [무료 PDF 뷰어 다운로드]
  • ※ 본 자료는 참고용 논문으로 수정 및 텍스트 복사가 되지 않습니다.
  • 구매가격
    135원 (구매자료 3% 적립)
    이메일 발송  스크랩 하기
    자료 다운로드  네이버 로그인
    Cooray and Ananda (2005) proposed a composite lognormal-Pareto model to analyze loss payment data in the actuarial and insurance industries. Their model is based on a lognormal density up to an unknown threshold value and a two-parameter Pareto density. In this paper, we implement the minimum density power divergence estimation for the composite lognormal-Pareto density. We compare the performances of the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) by simulations and an example. The minimum density power divergence estimator performs reasonably well against various violations in the distribution. The minimum density power divergence estimator better fits small observations and better resists against extraordinary large observations than the maximum likelihood estimator.
    사업자등록번호 220-87-87785 대표.신현웅 주소.서울시 서초구 방배로10길 18, 402호 대표전화.070-8809-9397
    개인정보책임자.박정아 통신판매업신고번호 제2017-서울서초-1765호 이메일 help@reportshop.co.kr
    copyright (c) 2009 happynlife. steel All reserved.