English  |  正體中文  |  简体中文  |  Items with full text/Total items : 78852/78852 (100%)
Visitors : 35562523      Online Users : 298
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU 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://ir.lib.ncu.edu.tw/handle/987654321/7742

    Title: 不需常態假設與不受離群值影響的選擇迴歸模型的方法;Selecting "good" regression models:an approach which is insensitive to normality and to outliers.
    Authors: 黃麟凱;Lin-kai Huang
    Contributors: 統計研究所
    Keywords: 離群值;選模統計量;強韌概似函數;Robust likelihood function;Outlier;Model selection statistic
    Date: 2009-06-03
    Issue Date: 2009-09-22 11:03:43 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 在線性複迴歸的架構下,Cp是常用的選模統計量。本文透過常態實作模型,使用Royall and Tsou (2003)的強韌概似函數方法,建構Cp的強韌修正項ATp。經由模擬的發現,ATp除了修正Cp的一些缺陷外,在違反常態假設時,表現比Cp另外的修正項RCp(Ronchetti and Staudte, 1994)與RTp(Sommer and Huggins, 1996)好。此外,本文亦將ATp應用在兩個實例上。 Base on multiple linear regression, the statistic, Cp, is usually used to do model selection. In this thesis, we use the robust likelihood technique introduced by Royall and Tsou (2003) to construct a roubust Cp (ATp) under the normal working model. By way of simulations, ATp not only adjusts some defects on Cp, but also is better than RCp (Ronchetti and Staudte, 1994) and RTp (Sommer and Huggins, 1996) which are the other roubust Cp statistics if the normal assumption is wrong. In addition, we use two real examples to demonstate.
    Appears in Collections:[統計研究所] 博碩士論文

    Files in This Item:

    File SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明