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ROBUST VARIABLE SELECTION IN REGRESSION IN THE PRESENCE OF OUTLIERS AND LEVERAGE POINTS
Author(s) -
Sommer Suzanne,
Staudte Robert G.
Publication year - 1995
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1995.tb00663.x
Subject(s) - outlier , leverage (statistics) , robust regression , statistics , regression analysis , regression , selection (genetic algorithm) , least trimmed squares , feature selection , variable (mathematics) , econometrics , linear regression , computer science , mathematics , artificial intelligence , nonlinear regression , mathematical analysis
Summary In linear regression, outliers and leverage points often have large influence in the model selection process. Such cases are downweighted with Mallows‐type weights here, during estimation of submodel parameters by generalised M‐estimation. A robust version of Mallows's Cp (Ronchetti &. Staudte, 1994) is then used to select a variety of submodels which are as informative as the full model. The methodology is illustrated on a new dataset concerning the agglomeration of alumina in Bayer precipitation.