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PreprintCC BY 4.0arXiv

Lehmer-Lambert Distribution

  • Masoud Ataei
  • Vladimir V. Vinogradov
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Abstract

The Lehmer transform of a positive signal is the ratio of its power sums at consecutive orders, read as a function of the order, and it is a strictly increasing analytic curve from the smallest observation to the largest. Composing this curve with a map whose inverse is the Lambert function turns it into a probability law on the real line, the Lehmer-Lambert distribution, which we introduce and study. Its distribution, density and quantile functions are closed-form, its random variates are exact, and the law is invariant under changes of gain and of power-law calibration of the signal. Its modes lie at orders at which the signal changes scale, and for a sample its tails are exponential with rates equal to the two extreme logarithmic gaps of the sample. We derive moments, generating functions and limit theorems, and we investigate applications of the Lehmer-Lambert distribution in the analysis of electroencephalograms of patients with major depressive disorder.

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Cite

BibTeXRIS
BibTeX
@misc{ataei2026lehmerlambert,
  title = {{Lehmer-Lambert Distribution}},
  author = {Masoud Ataei and Vladimir V. Vinogradov},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.12150},
  eprint = {2609.12150},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.12150},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Lehmer-Lambert Distribution
AU  - Masoud Ataei
AU  - Vladimir V. Vinogradov
PY  - 2026
DA  - 2026/09/10
PB  - arXiv
DO  - 10.48550/arxiv.2609.12150
UR  - https://arxiv.org/abs/2609.12150
AB  - The Lehmer transform of a positive signal is the ratio of its power sums at consecutive orders, read as a function of the order, and it is a strictly increasing analytic curve from the smallest observation to the largest. Composing this curve with a map whose inverse is the Lambert function turns it into a probability law on the real line, the Lehmer-Lambert distribution, which we introduce and study. Its distribution, density and quantile functions are closed-form, its random variates are exact, and the law is invariant under changes of gain and of power-law calibration of the signal. Its modes lie at orders at which the signal changes scale, and for a sample its tails are exponential with rates equal to the two extreme logarithmic gaps of the sample. We derive moments, generating functions and limit theorems, and we investigate applications of the Lehmer-Lambert distribution in the analysis of electroencephalograms of patients with major depressive disorder.
ER  -