English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a new method for offline change-point detection onsome parameters of the distribution of a random vector. We introduce apenalized maximum likelihood approach that can be efficiently computed by adynamic programming algorithm or approximated by a fast greedy binary splittingalgorithm. We prove both algorithms converge almost surely to the set ofchange-points under very general assumptions on the distribution andindependent sampling of the random vector. In particular, we show theassumptions leading to the consistency of the algorithms are satisfied bycategorical and Gaussian random variables. This new approach is motivated bythe problem of identifying homozygosity islands on the genome of individuals ina population. Our method directly tackles the issue of identification of thehomozygosity islands at the population level, without the need of analyzingsingle individuals and then combining the results, as is made nowadays instate-of-the-art approaches.

Population based change-point detection for the identification of homozygosity islands