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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/41185

    Title: Estimation for the generalized Pareto distribution with censored data
    Authors: 林千代;Lin, Chien-tai;Wang, Wen-yen
    Contributors: 淡江大學數學學系
    Keywords: Maximum likelihood;parameter estimation;statistical computing
    Date: 2000-11-01
    Issue Date: 2010-01-28 06:52:45 (UTC+8)
    Publisher: Taylor & Francis
    Abstract: We present a methodology for computing the point and interval maximum likelihood parameter estimation for the two-parameter generalized Pareto distribution (GPD) with censored data. The basic idea underlying our method is a reduction of the two-dimensional numerical search for the zeros of the GPD log-likelihood gradient vector to a one-dimensional numerical search. We describe a computationally efficient algorithm which implement this approach. Two illustrative examples are presented. Simulation results indicate that the estimates derived by maximum likelihood estimation are more reliable against those of method of moments. An evaluation of the practical sample size requirements for the asymptotic normality is also included.
    Relation: Communications in Statistics: Simulation and Computation 29(4), pp.1183-1213
    DOI: 10.1080/03610910008813660
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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