A unified approximation framework for the log-likelihood ratio on polynomial/logarithmic/fractional-power bases using moments up to order 3s adapts CUSUM/GRSh/SRP procedures to non-Gaussian change-point detection and is claimed to work on extremely heavy-tailed data where classical methods fail.
Kunchenko's Polynomials for Template Matching
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper reviews Kunchenko's polynomials using as template matching method to recognize template in one-dimensional input signal. Kunchenko's polynomials method is compared with classical methods - cross-correlation and sum of squared differences according to numerical statistical example.
fields
stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
This work traces the evolution of Kunchenko stochastic polynomials as a semiparametric methodology for non-Gaussian estimation, linking them formally to Volterra series while outlining the school's dissertations, collaborations, and an R package.
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Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection
A unified approximation framework for the log-likelihood ratio on polynomial/logarithmic/fractional-power bases using moments up to order 3s adapts CUSUM/GRSh/SRP procedures to non-Gaussian change-point detection and is claimed to work on extremely heavy-tailed data where classical methods fail.
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From Volterra Series to Kunchenko Stochastic Polynomials: Half a Century of Non-Gaussian Estimation Methodology
This work traces the evolution of Kunchenko stochastic polynomials as a semiparametric methodology for non-Gaussian estimation, linking them formally to Volterra series while outlining the school's dissertations, collaborations, and an R package.