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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A model selection approach for multiple sequence segmentation and dimensionality reduction

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Author(s):
Castro, Bruno M. [1] ; Lemes, Renan B. [2] ; Cesar, Jonatas [2] ; Hunemeier, Tabita [2] ; Leonardi, Florencia [3]
Total Authors: 5
Affiliation:
[1] Univ Fed Rio Grande do Norte, Dept Estat, Natal, RN - Brazil
[2] Univ Sao Paulo, Inst Biociencias, Sao Paulo - Brazil
[3] Univ Sao Paulo, Inst Matemat & Estat, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF MULTIVARIATE ANALYSIS; v. 167, p. 319-330, SEP 2018.
Web of Science Citations: 0
Abstract

In this paper we consider the problem of segmenting n aligned random sequences of equal length m into a finite number of independent blocks. We propose a penalized maximum likelihood criterion to infer simultaneously the number of points of independence as well as the position of each point. We show how to compute exactly the estimator by means of a dynamic programming algorithm with time complexity O(m(2)n). We also propose another method, called hierarchical algorithm, that provides an approximation to the estimator when the sample size increases and runs in time O[m In(m)n]. Our main theoretical results are the strong consistency of both estimators when the sample size n grows to infinity. We illustrate the convergence of these algorithms through some simulation examples and we apply the method to identify recombination hotspots in real SNPs data. (C) 2018 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Oswaldo Baffa Filho
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/17394-0 - Structure selection for stochastic processes in high dimensions
Grantee:Florencia Graciela Leonardi
Support Opportunities: Regular Research Grants