Robustifying Model Fitting

A robust method for fitting a model from a parametric family is fundamental to effective statistical analysis. A procedure is given for robustifying any model fitting process by using weights from the family from which the model is to be chosen. The weighting reduces the influence of information tha...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society. Series B, Methodological Methodological, 1995, Vol.57 (3), p.599-609
Main Author: Windham, Michael P.
Format: Article
Language:English
Subjects:
Estimation methods
Estimators
Exact sciences and technology
Gaussian distributions
iterative reweighting
Mathematical robustness
Mathematics
maximum likelihood
Maximum likelihood estimation
Maximum likelihood estimators
method of moments
Modeling
m‐estimation
Parametric inference
Parametric models
Point estimators
Probability and statistics
robust parameter estimation
Sciences and techniques of general use
Statistical analysis
Statistical methods
Statistical models
Statistical variance
Statistics
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Summary:A robust method for fitting a model from a parametric family is fundamental to effective statistical analysis. A procedure is given for robustifying any model fitting process by using weights from the family from which the model is to be chosen. The weighting reduces the influence of information that is not compatible with the model family, while maintaining the basic structure of a familiar model fitting process. The procedure produces a parametric family of model fitting functions. The value of the parameter determines the degree to which the weighting influences the robustified model fit. A mechanism for determining an appropriate value for the parameter is described.
ISSN:0035-9246
1369-7412
2517-6161
1467-9868
DOI:10.1111/j.2517-6161.1995.tb02050.x