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

This paper investigates the asymptotic properties of parameter estimation for the Ewens–Pitman partition with parameters $0<\alpha <1$ and $\theta >-\alpha$. Especially, we show that the maximum likelihood estimator (MLE) of $\alpha$ is $n^{\alpha \mathrm{/}2}$-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 $\alpha$. Our proof relies on a stable martingale central limit theorem, which is of independent interest.