000150696 001__ 150696
000150696 005__ 20251017144641.0
000150696 0247_ $$2doi$$a10.1016/j.cam.2025.116543
000150696 0248_ $$2sideral$$a142868
000150696 037__ $$aART-2025-142868
000150696 041__ $$aeng
000150696 100__ $$aBaz, Juan
000150696 245__ $$aEstimation of the covariance matrix of a Gaussian Markov Random Field under a total positivity constraint
000150696 260__ $$c2025
000150696 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150696 5203_ $$aGaussian Markov Random Fields are a popular statistical model that has been used successfully in many fields of application. Recent work has studied conditions under which the covariance matrix of a Gaussian Markov Random Field over a graph of paths is totally positive. In such case, many linear algebra operations concerning the covariance matrix can be performed with High Relative Accuracy (the relative error is of order of machine precision). Unfortunately, classical estimators of the covariance matrix do not necessarily yield a totally positive matrix, even when the population covariance matrix is totally positive. Essentially, this inconvenience prevents the available High Relative Accuracy methods to be used with real-life data. Here, we present a method for the estimation of the covariance matrix of a Gaussian Markov Random Field over a graph of paths assuring the estimated covariance matrix (or its inverse) is totally positive.
000150696 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E41-23R$$9info:eu-repo/grantAgreement/ES/MCIU/PID2022-138569NB-I00$$9info:eu-repo/grantAgreement/ES/MCIU/PID2022-139886NB-I00$$9info:eu-repo/grantAgreement/ES/MCIU/PID2022-140585NB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/RED2022-134176-T
000150696 540__ $$9info:eu-repo/semantics/embargoedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000150696 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000150696 700__ $$aAlonso, Pedro
000150696 700__ $$0(orcid)0000-0002-1340-0666$$aPeña, Juan Manuel$$uUniversidad de Zaragoza
000150696 700__ $$aPérez-Fernández, Raúl
000150696 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000150696 773__ $$g464 (2025), 116543 [15 pp.]$$pJ. comput. appl. math.$$tJournal of Computational and Applied Mathematics$$x0377-0427
000150696 8564_ $$s537974$$uhttps://zaguan.unizar.es/record/150696/files/texto_completo.pdf$$yPreprint$$zinfo:eu-repo/date/embargoEnd/2026-07-30
000150696 8564_ $$s1812924$$uhttps://zaguan.unizar.es/record/150696/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint$$zinfo:eu-repo/date/embargoEnd/2026-07-30
000150696 909CO $$ooai:zaguan.unizar.es:150696$$particulos$$pdriver
000150696 951__ $$a2025-10-17-14:31:52
000150696 980__ $$aARTICLE