A Robust Estimation for the Composite Lognormal-Pareto Model
분야
자연과학 > 통계학
저자
( Ro Jin Pak )
발행기관
한국통계학회
간행물정보
CSAM(Communications for Statistical Applications and Methods) 2013년, 제20권 제4호, 311~319페이지(총9페이지)
파일형식
02706321.pdf [무료 PDF 뷰어 다운로드]
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    영문초록
    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.
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