English  |  正體中文  |  简体中文  |  Items with full text/Total items : 52068/87197 (60%)
Visitors : 8907578      Online Users : 83
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: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/20080

    Title: A Stratification for Multiple Populations
    Authors: Chang, Horng-jinh;Shieh, Shuen-chin
    Contributors: 淡江大學經營決策學系
    Keywords: Hessian Matrix;Multiple Population;Neyman Allocatin;Optimum Stratification;Optimum stratification Points
    Date: 1990-12-01
    Issue Date: 2009-11-30 12:32:50 (UTC+8)
    Publisher: Taipei : Graduate Institute of Management Science, Tamkang University
    Abstract: An attempt will be made here to extend Dalenius'(1950) theory of univariate stratification for one population to multiple population. If k mutiple populations have the same or different domains, then they can be divided simultaneously into L strata according to the variable y under study. The gross population is composed of the k populations. The ith stratified simple random sample of size $n_i$ ia drawn from the ith population,i= 1, 2,...,k. The gross population mean is estimated by the k sample means. The variance of the gross sample mean will be taken as a measure. We will call a system of stratification, if it minimizes the variance of the sample mean. This paper will discuss the method for finding optimum stratification points for multiple populations under Neyman allocation. A numerical illustration is also given.
    Relation: International journal of information and management Sciences 1(2), pp.1-8
    Appears in Collections:[管理科學學系暨研究所] 期刊論文
    [資訊管理學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat

    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