Handy Formulas for Binomial Moments

TL;DR

This paper introduces new, simplified formulas for higher-order binomial moments expressed as polynomials in variance, along with algorithms and Python implementation, improving understanding and computation of these moments.

Contribution

It presents novel structured formulas for binomial moments in terms of variance, with algorithms and implementation, and provides sharp asymptotic estimates for central binomial moments.

Findings

New formulas are simpler and better structured than previous ones.

Algorithms and Python code for deriving binomial moments are provided.

Asymptotically sharp estimates for central binomial moments are established.

Abstract

Despite the relevance of the binomial distribution for probability theory and applied statistical inference, its higher-order moments are poorly understood. The existing formulas are either not general enough, or not structured and simplified enough for intended applications. This paper introduces novel formulas for binomial moments, in form of \emph{polynomials in the variance} rather than in the success probability. The obtained formulas are arguably better structured, simpler and superior in their numerical properties compared to prior works. In addition, the paper presents algorithms to derive these formulas along with working implementation in the Python symbolic algebra package. The novel approach is a combinatorial argument coupled with clever algebraic simplifications which rely on symmetrization theory. As an interesting byproduct we establish \emph{asymptotically sharp estimates for central binomial moments}, improving upon partial results from prior works.