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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/3854


    Title: 資料之變數轉換的穩健估計(2/2)
    Other Titles: Robust Estimation in Data Transformations (II)
    Authors: 鄭宗記
    Keywords: Box-Cox轉換;離群值偵測;前進搜尋演算法;高破壞點估計式;穩健估計
    Boc-Cox transformation;Detection of multiple outliers;High breakdown point;Robust estimation
    Date: 2001
    Issue Date: 2007-04-18 16:36:49 (UTC+8)
    Publisher: 臺北市：國立政治大學統計學系
    Abstract: 常態分配之假設在迴歸與多變量分析中提供了一個方便且有力的途徑，當資料不是常態時，一個適當的變數轉換可使問題簡單化，亦即藉由變數轉換的過程使資料符合常態的假設，例如Box-Cox 轉 換 (1964)。另一方面有時僅是因資料中所存 在的一個觀測值或數個觀測值，則必須考慮對變數做轉換。也就是說，變數轉換的過程極易受到離群值的影響。本研究之主要目的，在於利用穩健統計估計方法，使多變量資料在轉換的過程不受 到離群值的影響。因此，我們提出一個穩健檢定統計量，其結果將使資料之轉換結果符合常態假設，並提供轉換後之穩健估計，並判斷觀測值在轉換後是否為離群值。
    The assumption of normality provides the customary powerful and convenient way of analyzing linear regression problem and multivariate data. The problem of non-normality may often be simplified by an appropriate transformation, e.g. the parametric family of power transformations of Box and Cox (1964). The evidence for transformations may sometimes depend crucially on e one or a few observations. Therefore, multivariate data transformations are very sensitive to outliers. The purpose of the paper is to develop methods that would not be influenced by potential outliers during the process of data transformations. They essentially need robust statistics. We propose a robust likelihood ratio test for the transformation parameters. The resulting methods will be able to verify the role of every observation playing in the data transformation as well as to provide the robust estimation after transformation.
    Description: 核定金額：226000元
    Data Type: report
    Appears in Collections:[統計學系] 國科會研究計畫

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