Compound Poisson Point Processes, Concentration and Oracle Inequalities

TL;DR

This paper presents new theoretical insights into compound Poisson point processes, including characterizations and concentration inequalities, with applications to negative binomial regression and oracle inequalities for Lasso estimators.

Contribution

It introduces a novel characterization of discrete compound Poisson processes and derives concentration inequalities, extending existing work and enabling new statistical applications.

Findings

New characterization of discrete compound Poisson process

Derived concentration inequalities for count data models

Established oracle inequalities for penalized regression methods

Abstract

This note aims at presenting several new theoretical results for the compound Poisson point process, which follows the work of Zhang \emph{et al.} [Insurance~Math.~Econom.~59(2014), 325-336]. The first part provides a new characterization for a discrete compound Poisson point process (proposed by {Acz{\'e}l} [Acta~Math.~Hungar.~3(3)(1952), 219-224]), it extends the characterization of the Poisson point process given by Copeland and Regan [Ann.~Math.~(1936): 357-362]. Next, we derive some concentration inequalities for discrete compound Poisson point process (negative binomial random variable with unknown dispersion is a significant example). These concentration inequalities are potentially useful in count data regressions. We give an application in the weighted Lasso penalized negative binomial regression whose KKT conditions of penalized likelihood hold with high probability and then we derive non-asymptotic oracle inequalities for a weighted Lasso estimator.