Approximating moments of continuous functions of random variables using Bernstein polynomials

Bernstein polynomials have many interesting properties. In statistics, they were mainly used to estimate density functions and regression relationships. The main objective of this paper is to promote further use of Bernstein polynomials in statistics. This includes (1) providing a high-level approximation of the moments of a continuous function g(X)g(X) of a random variable XX, and (2) proving Jensen’s inequality concerning a convex function without requiring second differentiability of the function. The approximation in (1) is demonstrated to be quite superior to the delta method  , which is used to approximate the variance of g(X)g(X) with the added assumption of differentiability of the function. Two numerical examples are given to illustrate the application of the proposed methodology in (1).