Mixed exponential statistical structures and their approximation operators

TL;DR

This paper introduces a new class of mixed exponential statistical structures and their approximation operators, unifying continuous and discrete stochastic models with potential for advanced analytical and approximation applications.

Contribution

It presents a generalized family of mixed exponential structures and operators, including known cases, with detailed analysis of their properties and convergence behavior.

Findings

Recurrent relations for central moments established

Operators demonstrate controllable approximation properties

Unified framework for continuous and discrete stochastic models

Abstract

The paper examines the construction and analysis of a new class of mixed exponential statistical structures that combine the properties of stochastic models and linear positive operators. The relevance of the topic is driven by the growing need to develop a unified theoretical framework capable of describing both continuous and discrete random structures that possess approximation properties. The aim of the study is to introduce and analyze a generalized family of mixed exponential statistical structures and their corresponding linear positive operators, which include known operators as particular cases. We define auxiliary statistical structures B and H through differential relations between their elements, and construct the main Phillips-type structure. Recurrent relations for the central moments are obtained, their properties are established, and the convergence and approximation accuracy of the constructed operators are investigated. The proposed approach allows mixed exponential structures to be viewed as a generalization of known statistical systems, providing a unified analytical and stochastic description. The results demonstrate that mixed exponential statistical structures can be used to develop new classes of positive operators with controllable preservation and approximation properties. The proposed methodology forms a basis for further research in constructing multidimensional statistical structures, analyzing operators in weighted spaces, and studying their asymptotic characteristics.