Recent advance in sensitivity analysis in multivariate statistical methods
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- TANAKA Y.
- Department of Environmental and Mathmatical Sciences, Okayama University
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抄録
Methodologies have been developed in the last two decades for detecting influential observations and evaluating the stability of the results of analysis not only in regression and related methods but also in other multivariate methods. In developing these methodologies influence functions play important roles. The present paper shows that influence functions can be derived in various multivariate statistical methods and that a general strategy based on influence functions and its robust version are useful for detecting singly and/or jointly influential observations. Cases of principal component analysis, exploratory factor analysis and confirmatory factor analysis are studied with numerical examples.
収録刊行物
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- Journal of the Japanese Society of Computational Statistics
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Journal of the Japanese Society of Computational Statistics 7 1-25, 1994
日本計算機統計学会
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詳細情報
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- CRID
- 1573950401674453888
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- NII論文ID
- 110001235608
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- NII書誌ID
- AA10823693
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- ISSN
- 09152350
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- 本文言語コード
- en
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- データソース種別
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- CiNii Articles