Stochastic Taylor expansion via Poisson point processes

Fuente: arXiv
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Hauptverfasser: Wu, Weichao, Micheas, Athanasios C.
Format: Preprint
Veröffentlicht: 2025
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author Wu, Weichao
Micheas, Athanasios C.
author_facet Wu, Weichao
Micheas, Athanasios C.
contents We generalize Taylor's theorem by introducing a stochastic formulation based on an underlying Poisson point process model. We utilize this approach to propose a novel non-linear regression framework and perform statistical inference of the model parameters. Theoretical properties of the proposed estimator are also proven, including its convergence, uniformly almost surely, to the true function. The theory is presented for the univariate and multivariate cases, and we exemplify the proposed methodology using several examples via simulations and an application to stock market data.
format Preprint
id arxiv_https___arxiv_org_abs_2508_04703
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Stochastic Taylor expansion via Poisson point processes
Wu, Weichao
Micheas, Athanasios C.
Methodology
Statistics Theory
Applications
We generalize Taylor's theorem by introducing a stochastic formulation based on an underlying Poisson point process model. We utilize this approach to propose a novel non-linear regression framework and perform statistical inference of the model parameters. Theoretical properties of the proposed estimator are also proven, including its convergence, uniformly almost surely, to the true function. The theory is presented for the univariate and multivariate cases, and we exemplify the proposed methodology using several examples via simulations and an application to stock market data.
title Stochastic Taylor expansion via Poisson point processes
topic Methodology
Statistics Theory
Applications
url https://arxiv.org/abs/2508.04703