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Outliers in Multivariate Regression Models

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  • Srivastava, Muni S.
  • von Rosen, Dietrich

Abstract

Likelihood ratio tests for detecting a single outlier in multivariate linear models are considered, where an observation is called an outlier if there has been a shift in the mean. The test statistics are the maximum of n nonindependent statistics, where n is the number of observations. Relevant distributions to use upper and lower Bonferroni's inequalities are given.

Suggested Citation

  • Srivastava, Muni S. & von Rosen, Dietrich, 1998. "Outliers in Multivariate Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 195-208, May.
    • Handle: RePEc:eee:jmvana:v:65:y:1998:i:2:p:195-208
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    References listed on IDEAS

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    1. Khatri, C. G., 1979. "Characterizations of multivariate normality II. Through linear regressions," Journal of Multivariate Analysis, Elsevier, vol. 9(4), pages 589-598, December.
    2. Minoru Siotani, 1959. "The extreme value of the generalized distances of the individual points in the multivariate normal sample," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 10(3), pages 183-208, September.
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    Cited by:

    1. Toshiaki Tsukurimichi & Yu Inatsu & Vo Nguyen Le Duy & Ichiro Takeuchi, 2022. "Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1197-1228, December.

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