Pattern expansions of permutation statistics
Ian Cavey, Hugh Dennin, Bridget Eileen Tenner
TL;DR
The paper develops a unified framework for expressing permutation statistics as pattern-count expansions and analyzes when these expansions are finite or nonnegative. It proves a general averaging criterion via marked permutations that yields pattern-finiteness for broad classes of statistics, including higher moment statistics with coefficients tied to Stirling numbers. It then introduces an inclusion-exclusion approach to obtain pattern-positivity, with the number of reduced words $\\mathsf{rw}(w)$ as a central case; this yields an explicit, combinatorially interpretable expansion and a decomposition of $RW(w)$ into pattern-indexed contributions. Together, these results offer new tools for bounding and interpreting permutation statistics through pattern-level data and suggest rich avenues for connecting pattern expansions to classical algebraic combinatorics questions such as Schubert positivity.
Abstract
We study the expansions of permutation statistics in the basis of functions counting occurrences of a fixed pattern in a permutation. We show the finiteness of these pattern expansions for a class of permutation statistics including the higher moment statistics, generalizing a result of Berman and Tenner. We also give a combinatorial criterion for the positivity of pattern expansions. Using this criterion, we show that the pattern expansion of the number of reduced words of a permutation is positive and give an enumerative interpretation for the coefficients.
