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Nonparametric Estimation of the Laplace Transform from Empirical Data Using Symmetric Kernels Method

Year: 2025

Type: article

Abstract: Focusing on the use of a Gaussian kernel, the paper constructs a nonparametric Laplace transform estimator via symmetric kernel methodology. Convergence rates and an asymptotic mean integrated squared error (AMISE) formula are used to establish theoretical properties of the estimator and guide optimal bandwidth choice. Finite-sample performance is evaluated through comprehensive simulations. Real and synthetic data applications illustrate practical utility. Estimator robustness and accuracy are showed in outcomes, interpretation the implement valuable across statistical and practical fields.

Source: Journal of Advances in Mathematics and Computer Science

DOI: 10.9734/jamcs/2025/v40i62008

Authors: Raogo Frank Emile er Jumeau KABORE, Sobom Matthieu SOME, Abel ZONGO, S. Pierre Clovis NITIEMA

Institutions: Université Joseph Ki-Zerbo, Université Thomas SANKARA

Language: English

Topic: Probability and Statistical Research

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4410924727

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