Decompounding Under General Mixing Distributions
arxiv(2024)
Abstract
This study focuses on statistical inference for compound models of the form
X=ξ_1+…+ξ_N, where N is a random variable denoting the count of
summands, which are independent and identically distributed (i.i.d.) random
variables ξ_1, ξ_2, …. The paper addresses the problem of
reconstructing the distribution of ξ from observed samples of X's
distribution, a process referred to as decompounding, with the assumption that
N's distribution is known. This work diverges from the conventional scope by
not limiting N's distribution to the Poisson type, thus embracing a broader
context. We propose a nonparametric estimate for the density of ξ, derive
its rates of convergence and prove that these rates are minimax optimal for
suitable classes of distributions for ξ and N. Finally, we illustrate the
numerical performance of the algorithm on simulated examples.
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