 Robust tests for linear regression models based on τ-estimatesMatias Salibian-Barrera, Stefan Van Aelst and Víctor J. Yohai Computational Statistics & Data Analysis, 2016, vol. 93, issue C, 436-455 Abstract: ANOVA tests are the standard tests to compare nested linear models fitted by least squares. These tests are equivalent to likelihood ratio tests, so they have high power. However, least squares estimators are very vulnerable to outliers in the data, and thus the related ANOVA type tests are also extremely sensitive to outliers. Therefore, robust estimators can be considered to obtain a robust alternative to the ANOVA tests. Regression τ-estimators combine high robustness with high efficiency which makes them suitable for robust inference beyond parameter estimation. Robust likelihood ratio type test statistics based on the τ-estimates of the error scale in the linear model are a natural alternative to the classical ANOVA tests. The higher efficiency of the τ-scale estimates compared with other robust alternatives is expected to yield tests with good power. Their null distribution can be estimated using either an asymptotic approximation or the fast and robust bootstrap. The robustness and power of the resulting robust likelihood ratio type tests for nested linear models is studied. Keywords: Robust statistics; Robust tests; Linear regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:93:y:2016:i:c:p:436-455 DOI: 10.1016/j.csda.2014.09.012 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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