A CERTAIN CLASS OF STATISTICAL PROBABILITY CONVERGENCE AND ITS APPLICATIONS TO APPROXIMATION THEOREMS

In the present work, we introduce and study the notion of statistical probability convergence for sequences of random variables as well as the concept of statistical convergence for sequences of real numbers, which are defined over a Banach space via deferred weighted summability mean. We first establish a theorem presenting a connection between them. Based upon our proposed methods, we then prove a new Korovkin-type approximation theorem with periodic test functions for a sequence of random variables on a Banach space and demonstrate that our theorem effectively extends and improves most (if not all) of the previously existing results (in statistical versions). We also estimate the rate of deferred weighted statistical probability convergence and accordingly establish a new result. Finally, an illustrative example is presented here by means of the generalized Fejer convolution operators of a sequence of random variables in order to demonstrate that our established theorem is stronger than its traditional and statistical versions.