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2023•BernoulliRequires access

Necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators

Jie Chang, Michael Grabchak

Open publisher page 3 citations

Abstract

This paper establishes necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators. It further gives several easy to verify sufficient conditions. These conditions are then used to show that the assumptions hold for large classes of distributions with regularly varying tails. This includes classes for which asymptotic normality had not been previously verified.

About this research paper

What this paper is about

This paper establishes necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators. It further gives several easy to verify sufficient conditions. These conditions are then used to show that the assumptions hold for large classes of distributions with regularly varying tails. This includes classes for which asymptotic normality had not been previously verified.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper establishes necessary and sufficient conditions for the asymptotic normality of higher order Turing estimators. It further gives several easy to verify sufficient conditions. These conditions are then used to show that the assumptions hold for large classes of distributions with regularly varying tails. This includes classes for which asymptotic normality had not been previously verified.

Key concepts: Mathematics, Asymptotic distribution, Normality, Estimator, Turing, Local asymptotic normality, Applied mathematics, Asymptotic analysis

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