English  |  正體中文  |  简体中文  |  Items with full text/Total items : 59108/92571 (64%)
Visitors : 735127      Online Users : 36
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/987654321/20729


    Title: Testing goodness of fit for a parametric family of link functions
    Authors: Cheng, K. F.;吳忠武;Wu, Jong-wuu
    Contributors: 淡江大學統計學系
    Keywords: Consistency of the test;Dimension reduction;Link function;Quasi-likelihood model;Regression parameters
    Date: 1994-06-01
    Issue Date: 2009-11-30 12:57:58 (UTC+8)
    Publisher: American Statistical Association
    Abstract: We concern ourselves with the methods for testing the overall goodness of fit of a parametric family of link functions used for modeling the conditional mean of the response variable Y given the covariates X = x ∈ R P. The null hypothesis is that the conditional mean function is a known functional depending on βx and a finite number of parameters θ = (θ1,…, θq), where β is a p-dimensional row vector of regression parameters and x is a column vector. The proposed test statistic is derived from an “information” equivalence result and a dimension-reduction technique. The new test is very simple in computation. Also, it is generally consistent against broad class of alternatives and, asymptotically, the null distribution is independent of the underlying distribution of Y, given X = x. Practical examples are given to show the advantage of the proposed test. Furthermore, power comparisons with the test used by Su and Wei are also performed to indicate the usefulness of the new test. Particularly, we find that the new test has good power performance in discriminating between the probit and logit links.
    Relation: Journal of the American Statistical Association 89(426), pp.657-664
    DOI: 10.2307/2290868
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

    Files in This Item:

    File SizeFormat
    index.html0KbHTML150View/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