Asymptotic mixed normality of maximum likelihood estimator for Ewens--Pitman partition

Fuente: arXiv
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Hauptverfasser: Koriyama, Takuya, Matsuda, Takeru, Komaki, Fumiyasu
Format: Preprint
Veröffentlicht: 2022
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author Koriyama, Takuya
Matsuda, Takeru
Komaki, Fumiyasu
author_facet Koriyama, Takuya
Matsuda, Takeru
Komaki, Fumiyasu
contents This paper investigates the asymptotic properties of parameter estimation for the Ewens--Pitman partition with parameters $0<α<1$ and $θ>-α$. Especially, we show that the maximum likelihood estimator (MLE) of $α$ is $n^{α/2}$-consistent and converges to a variance mixture of normal distributions, where the variance is governed by the Mittag-Leffler distribution. Moreover, we show that a proper normalization involving a random statistic eliminates the randomness in the variance. Building on this result, we construct an approximate confidence interval for $α$. Our proof relies on a stable martingale central limit theorem, which is of independent interest.
format Preprint
id arxiv_https___arxiv_org_abs_2207_01949
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Asymptotic mixed normality of maximum likelihood estimator for Ewens--Pitman partition
Koriyama, Takuya
Matsuda, Takeru
Komaki, Fumiyasu
Statistics Theory
Probability
This paper investigates the asymptotic properties of parameter estimation for the Ewens--Pitman partition with parameters $0<α<1$ and $θ>-α$. Especially, we show that the maximum likelihood estimator (MLE) of $α$ is $n^{α/2}$-consistent and converges to a variance mixture of normal distributions, where the variance is governed by the Mittag-Leffler distribution. Moreover, we show that a proper normalization involving a random statistic eliminates the randomness in the variance. Building on this result, we construct an approximate confidence interval for $α$. Our proof relies on a stable martingale central limit theorem, which is of independent interest.
title Asymptotic mixed normality of maximum likelihood estimator for Ewens--Pitman partition
topic Statistics Theory
Probability
url https://arxiv.org/abs/2207.01949