Statistical detection of patterns in unidimensional distributions by continuous wavelet transforms

Результат исследований: Научные публикации в периодических изданияхстатья

1 цитирование (Scopus)

Выдержка

Objective detection of specific patterns in statistical distributions, like groupings or gaps or abrupt transitions between different subsets, is a task with a rich range of applications in astronomy: Milky Way stellar population analysis, investigations of the exoplanets diversity, Solar System minor bodies statistics, extragalactic studies, etc. We adapt the powerful technique of the wavelet transforms to this generalized task, making a strong emphasis on the assessment of the patterns detection significance. Among other things, our method also involves optimal minimum-noise wavelets and minimum-noise reconstruction of the distribution density function. Based on this development, we construct a self-closed algorithmic pipeline aimed to process statistical samples. It is currently applicable to single-dimensional distributions only, but it is flexible enough to undergo further generalizations and development. (C) 2018 Elsevier B.V. All rights reserved.
Язык оригиналаанглийский
Страницы (с-по)151-165
ЖурналAstronomy and Computing
Том23
DOI
СостояниеОпубликовано - апр 2018

Отпечаток

Astronomy
Solar system
wavelet analysis
Wavelet transforms
Probability density function
Distribution functions
Pipelines
Statistics
extrasolar planets
statistical distributions
astronomy
solar system
set theory
density distribution
statistics

Цитировать

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Statistical detection of patterns in unidimensional distributions by continuous wavelet transforms. / Baluev, R.V.

В: Astronomy and Computing, Том 23, 04.2018, стр. 151-165.

Результат исследований: Научные публикации в периодических изданияхстатья

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