An adjusted Grubbs' and generalized extreme studentized deviation-Reference-Cited by-同舟云学术

An adjusted Grubbs' and generalized extreme studentized deviation

Published:2021-01-01 Issue:1 Volume:54 Page:548-557
ISSN:2391-4661
Container-title:Demonstratio Mathematica
language:en
Short-container-title:

Author:

Alrawashdeh Mufda Jameel¹

Affiliation:

1. Department of Mathematics, College of Sciences and Arts , Al-Ras, Qassim University , Kingdom of Saudi Arabia

Abstract

Abstract Detecting outlier data is an interesting subject in the statistical field. Grubbs’ test is one of the common detection methods of outlier observation at univariate data sets. This approach is based on the mean and standard deviation of univariate data, and hence, these data are highly affected by the presence of outliers. An improvement for Grubbs’ test is proposed in this paper to increase the power of detecting data.

Publisher

Walter de Gruyter GmbH

Subject

General Mathematics

Link

https://www.degruyter.com/document/doi/10.1515/dema-2021-0041/pdf

Reference30 articles.

1. V. Barnett and T. Lewis, Outliers in Statistical Data, Wiley Series in Probability and Statistics, New York, 1984.

2. F. Mosteller and J. W. Tukey, Data Analysis and Regression: A Second Course in Statistics, Addison-Wesley Ser. Math., 1977.

3. M. Hubert and E. Vandervieren, An adjusted boxplot for skewed distributions, Comput. Statist. Data Anal. 52 (2004), 5186–5201.

4. P. J. Rousseeuw and K. V. Driessen, A fast algorithm for the minimum covariance determinant estimator, Technometrics 41 (1999), 212–223.

5. P. M. Hubert, P. J. Rousseeuw, D. Vanpaemel, and T. Verdonck, The DetS and DetMM estimators for multivariate location and scatter, Comput. Statist. Data Anal. 81 (2015), 64–75.

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