Stopping models closed under pgf composition, and the stability of randomly stopped model extensions
Abstract
Statistical model transformations based on randomly stopped sums, maxima and minima are widely used to extend statistical models. We characterize the complete set of stopping models for which randomly stopped sum and extreme model transformations function as statistically stable (idempotent) model extensions. Stability requires the underlying stopping model to be closed under pgf composition. We prove that any finite-dimensional, connected stopping model closed under pgf composition is necessarily a family of random variables whose pgfs commute. Using the corresponding Koenigs function, we establish that these models form a statistical manifold admitting a global, one-dimensional parametrization θ= Pr(N=1) ∈ (0, θ*], where the probability mass at i is a polynomial in θ of degree at most i. Finally, we establish a duality between stopping models closed and containing the identity variable (the ones yielding stable extensions) and the set of probability distributions supported on the positive integers. These findings disprove the long standing conjecture that statistical stability occurs only under geometric stopping.
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BibTeX
@misc{valero2026stopping,
title = {{Stopping models closed under pgf composition, and the stability of randomly stopped model extensions}},
author = {Jordi Valero and Josep Ginebra},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30106},
eprint = {2609.30106},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30106},
}
RIS
TY - UNPB
TI - Stopping models closed under pgf composition, and the stability of randomly stopped model extensions
AU - Jordi Valero
AU - Josep Ginebra
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30106
UR - https://arxiv.org/abs/2609.30106
AB - Statistical model transformations based on randomly stopped sums, maxima and minima are widely used to extend statistical models. We characterize the complete set of stopping models for which randomly stopped sum and extreme model transformations function as statistically stable (idempotent) model extensions. Stability requires the underlying stopping model to be closed under pgf composition. We prove that any finite-dimensional, connected stopping model closed under pgf composition is necessarily a family of random variables whose pgfs commute. Using the corresponding Koenigs function, we establish that these models form a statistical manifold admitting a global, one-dimensional parametrization θ= Pr(N=1) ∈ (0, θ_*], where the probability mass at i is a polynomial in θ of degree at most i. Finally, we establish a duality between stopping models closed and containing the identity variable (the ones yielding stable extensions) and the set of probability distributions supported on the positive integers. These findings disprove the long standing conjecture that statistical stability occurs only under geometric stopping.
ER -