An effective selection of regression variables when the error distribution is incorrectly specified

DOI10.1007/BF02491488zbMath0641.62043OpenAlexW2103375873MaRDI QIDQ1100830

Wolfgang Karl Härdle

Publication date: 1987

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02491488

zbMATH Keywords

Monte Carlo studymaximum likelihoodrobust regressionmodel choiceasymptotically efficient selection of regression variablesMallows Cp-criterion

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

On Efficient AR Spectral Estimation for Long-Range Predictions

Cites Work

• Robust regression function estimation
• Asymptotics for M-type smoothing splines
• An asymptotically optimal window selection rule for kernel density estimates
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Statistical predictor identification
• How Many Variables Should be Entered in a Regression Equation?
• Robust analysis of variance based upon a likelihood ratio criterion
• An optimal selection of regression variables
• Robust Estimates of Location: Survey and Advances
• Some Comments on C P
• Robust Statistics

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