Moderate Deviations for Random-Indexed Cluster Counts in Hierarchical Pitman-Yor Models

Abstract

Consider a sample of size $N$ from a two-layer hierarchical Pitman--Yor model, and let $\xi (N)$ denote the random number of clusters generated at the first layer of the hierarchy. We study moderate deviation principles for partition statistics evaluated at the random sample size $\xi (N)$. In particular, we establish moderate deviation principles for the total number of clusters $K_{\xi (N)}$ and for the frequency count $M_{l,\xi (N)}$, the number of upper-level clusters represented by exactly $l$ first-level clusters. Our analysis combines fixed-sample-size small-tilt asymptotics with a moderate deviation principle for the random index $\xi (N)$. A uniform small-tilt argument, together with exponentially weighted tail estimates, allows us to pass from deterministic sample sizes to the hierarchical random-index setting. We obtain explicit deviation speeds and good rate functions on a family of intermediate scales between the typical growth order $N^{{\alpha }_1{\alpha }_2}$ and the linear scale $N$. The resulting rate functions exhibit a common power-law structure governed by the product ${\alpha }_1{\alpha }_2$, revealing a multiplicative effect of the two levels of the hierarchy.