Abstract
This chapter is devoted to analysis of risk robustness with respect to small-sample effects for commonly used plug-in decision rules derived from the Bayesian decision rule by substitution of unknown multivariate probability densities by their parametric estimates. These parametric estimates are constructed using the family of minimum contrast estimators of parameters (including ML-estimators, LS-estimators, etc.). We develop the method of asymptotic expansion for risk functional and construct general asymptotic expansions and approximations for the robustness factor. High accuracy of these approximations is demonstrated by computer experiments.
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© 1996 Springer Science+Business Media Dordrecht
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Kharin, Y. (1996). Robustness of Parametric Decision Rules and Small-sample Effects. In: Robustness in Statistical Pattern Recognition. Mathematics and Its Applications, vol 380. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8630-6_3
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DOI: https://doi.org/10.1007/978-94-015-8630-6_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4760-1
Online ISBN: 978-94-015-8630-6
eBook Packages: Springer Book Archive