Sign-based methods in linear statistical models
著者
書誌事項
Sign-based methods in linear statistical models
（Translations of mathematical monographs, v. 162）
American Mathematical Society, c1997
- タイトル別名
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Знаковые методы в линейных статистических моделях
Znakovye metody v lineĭnykh statisticheskikh modeli︠u︡kh
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注記
Includes bibliographical references (p. 231-234)
内容説明・目次
内容説明
For nonparametric statistics, the last half of this century was the time when rank-based methods originated, were vigorously developed, reached maturity, and received wide recognition. The rank-based approach in statistics consists in ranking the observed values and using only the ranks rather than the original numerical data. In fitting relationships to observed data, the ranks of residuals from the fitted dependence are used. The signed-based approach is based on the assumption that random errors take positive or negative values with equal probabilities. Under this assumption, the sign procedures are distribution-free. These procedures are robust to violations of model assumptions, for instance, to even a considerable number of gross errors in observations. In addition, sign procedures, have fairly high relative asymptotic efficiency, in spite of the obvious loss of information incurred by the use of signs instead of the corresponding numerical values. In this work, sign-based methods in the framework of linear models are developed. In the first part of the book, there are linear and factor models involving independent observations.
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