Statistics of a multi-factor function from its Fourier transform

Fuente: arXiv
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Autori principali: Herman, Matthew A., Doro, Stephen
Natura: Preprint
Pubblicazione: 2026
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author Herman, Matthew A.
Doro, Stephen
author_facet Herman, Matthew A.
Doro, Stephen
contents For a phenomenon $\boldsymbol{f}$ that is a function of $n$ factors, defined on a finite abelian group $G$, we derive its population statistics solely from its Fourier transform $\hat{\boldsymbol{f}}$. Our main result is an $m$-Coefficient/Index Annihilation Theorem: the $m$th moment of $\boldsymbol{f}$ becomes a series of terms, each with precisely $m$ Fourier coefficients --- and surprisingly, the coefficient indices in each term sum to zero under group addition. This condition acts like a filter, limiting which terms appear in the Fourier domain, and can reveal deeper relationships between the variables driving $\boldsymbol{f}$. These techniques can also be used as an analytical/design tool, or as a feasibility constraint in search algorithms. For functions defined on $\mathbb{Z}_2^n$, we show how the skew, kurtosis, etc. of a binomial distribution can be derived from the Fourier domain. Several other examples are presented.
format Preprint
id arxiv_https___arxiv_org_abs_2605_02248
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Statistics of a multi-factor function from its Fourier transform
Herman, Matthew A.
Doro, Stephen
Statistics Theory
Discrete Mathematics
Signal Processing
Genomics
Statistical Finance
62H10, 42B05
C.4; G.1.2; G.1.3; G.2; G.3; I.2.8; J.2; J.3; J.4
For a phenomenon $\boldsymbol{f}$ that is a function of $n$ factors, defined on a finite abelian group $G$, we derive its population statistics solely from its Fourier transform $\hat{\boldsymbol{f}}$. Our main result is an $m$-Coefficient/Index Annihilation Theorem: the $m$th moment of $\boldsymbol{f}$ becomes a series of terms, each with precisely $m$ Fourier coefficients --- and surprisingly, the coefficient indices in each term sum to zero under group addition. This condition acts like a filter, limiting which terms appear in the Fourier domain, and can reveal deeper relationships between the variables driving $\boldsymbol{f}$. These techniques can also be used as an analytical/design tool, or as a feasibility constraint in search algorithms. For functions defined on $\mathbb{Z}_2^n$, we show how the skew, kurtosis, etc. of a binomial distribution can be derived from the Fourier domain. Several other examples are presented.
title Statistics of a multi-factor function from its Fourier transform
topic Statistics Theory
Discrete Mathematics
Signal Processing
Genomics
Statistical Finance
62H10, 42B05
C.4; G.1.2; G.1.3; G.2; G.3; I.2.8; J.2; J.3; J.4
url https://arxiv.org/abs/2605.02248