English  |  正體中文  |  简体中文  |  Items with full text/Total items : 64002/96726 (66%)
Visitors : 3620200      Online Users : 311
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/19775


    Title: Bayesian sampling plans for exponential distribution based on uniform random censored data
    Authors: 黃文濤;Huang, Wen-tao;Lin, Yu-pin
    Contributors: 淡江大學經營決策學系
    Keywords: Bayes sampling plan;Exponential population;Uniform random censoring
    Date: 2004-01-28
    Issue Date: 2009-11-30 12:21:34 (UTC+8)
    Publisher: Elsevier
    Abstract: The problem of a single sampling plan with polynomial loss for the exponential distribution based on uniformly distributed random censored data has been considered. A Bayes sampling plan is derived under various schemes of censoring time. It is specially focused on a quadratic loss and an unit time cost is included in the loss. Some optimal Bayes solutions are tabulated and some numerical comparisons between the proposed plan and a known plan under special loss are also made. It is shown that the optimal solutions of the known plan are not Bayes in general.
    Relation: Computational Statistics and Data Analysis 44(4), pp.669-691
    DOI: 10.1016/S0167-9473(02)00330-4
    Appears in Collections:[Department of Management Sciences] Journal Article

    Files in This Item:

    File Description SizeFormat
    Bayesian sampling plans for exponential distribution based on uniform random censored data.pdf401KbAdobe PDF40View/Open
    index.html0KbHTML47View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback