2021/11/19 by Lucas Prates, Prates, Lucas, Renan Barbosa Lemes +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #FOS: Computer and information sciences #Genetic Associations and Epidemiology #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Machine Learning (stat.ML) #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2111.10187
openalex publication_date 2021/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a new method for offline change-point detection on some parameters of the distribution of a random vector. We introduce a penalized maximum likelihood approach that can be efficiently computed by a dynamic programming algorithm or approximated by a fast greedy binary splitting algorithm. We prove both algorithms converge almost surely to the set of change-points under very general assumptions on the distribution and independent sampling of the random vector. In particular, we show the assumptions leading to the consistency of the algorithms are satisfied by categorical and Gaussian random variables. This new approach is motivated by the problem of identifying homozygosity islands on the genome of individuals in a population. Our method directly tackles the issue of identification of the homozygosity islands at the population level, without the need of analyzing single individuals and then combining the results, as is made nowadays in state-of-the-art approaches.