Large and moderate deviation principles for nonparametric recursive kernel distribution estimators defined by stochastic approximation method

• Autorzy: Slaoui Yousri

Identyfikatory

DOI

10.7494/OpMath.2019.39.5.733

• Języki publikacji: EN

Abstrakty

EN

In this paper we prove large and moderate deviations principles for the recursive kernel estimators of a distribution function defined by the stochastic approximation algorithm. We show that the estimator constructed using the stepsize which minimize the Mean Integrated Squared Error (MISE) of the class of the recursive estimators defined by Mokkadem et al. gives the same pointwise large deviations principle (LDP) and moderate deviations principle (MDP) as the Nadaraya kernel distribution estimator.

Słowa kluczowe

EN

distribution estimation; stochastic approximation algorithm large and moderate deviations principles

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Opuscula Mathematica
• Rocznik: 2019
• Tom: Vol. 39, no. 5
• Strony: 733--746
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Slaoui Yousri

• University of Poitiers Laboratoire de Mathematiques et Applications UMR 7348 du CNRS Teleport 2 - BP 30179 11 Boulevard Marie et Pierre Curie 86962 Futuroscope Chasseneuil, France

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