Robust inference for skewed data in health sciences

Health data are often not symmetric to be adequately modeled through the usual normal distributions; most of them exhibit skewed patterns. They can indeed be modeled better through the larger family of skew-normal distributions covering both skewed and symmetric cases. Since outliers are not uncommo...

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Veröffentlicht in:Journal of applied statistics Jg. 49; H. 8; S. 2093 - 2123
Hauptverfasser: Nandy, Amarnath, Basu, Ayanendranath, Ghosh, Abhik
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Taylor & Francis 11.06.2022
Schlagworte:
Genetic algorithm
Influence function
robust minimum density power divergence estimation
Skew normal (SN) distribution
test for symmetry
wald-type test
robust minimum density power divergence estimation
Influence function
Skew normal (SN) distribution
test for symmetry
Genetic algorithm
wald-type test
ISSN:0266-4763, 1360-0532
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Zusammenfassung:Health data are often not symmetric to be adequately modeled through the usual normal distributions; most of them exhibit skewed patterns. They can indeed be modeled better through the larger family of skew-normal distributions covering both skewed and symmetric cases. Since outliers are not uncommon in complex real-life experimental datasets, a robust methodology automatically taking care of the noises in the data would be of great practical value to produce stable and more precise research insights leading to better policy formulation. In this paper, we develop a class of robust estimators and testing procedures for the family of skew-normal distributions using the minimum density power divergence approach with application to health data. In particular, a robust procedure for testing of symmetry is discussed in the presence of outliers. Two efficient computational algorithms are discussed. Besides deriving the asymptotic and robustness theory for the proposed methods, their advantages and utilities are illustrated through simulations and a couple of real-life applications for health data of athletes from Australian Institute of Sports and AIDS clinical trial data.
Bibliographie:ObjectType-Article-1
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ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2021.1891527