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Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models

High‐dimensional inference is one of fundamental problems in modern biomedical studies. However, the existing methods do not perform satisfactorily. Based on the Markov property of graphical models and the likelihood ratio test, this article provides a simple justification for the Markov neighborhoo...

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Detalles Bibliográficos
Autores principales: Sun, Lizhe, Liang, Faming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427730/
https://www.ncbi.nlm.nih.gov/pubmed/35688606
http://dx.doi.org/10.1002/sim.9493
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author Sun, Lizhe
Liang, Faming
author_facet Sun, Lizhe
Liang, Faming
author_sort Sun, Lizhe
collection PubMed
description High‐dimensional inference is one of fundamental problems in modern biomedical studies. However, the existing methods do not perform satisfactorily. Based on the Markov property of graphical models and the likelihood ratio test, this article provides a simple justification for the Markov neighborhood regression method such that it can be applied to statistical inference for high‐dimensional generalized linear models with mixed features. The Markov neighborhood regression method is highly attractive in that it breaks the high‐dimensional inference problems into a series of low‐dimensional inference problems. The proposed method is applied to the cancer cell line encyclopedia data for identification of the genes and mutations that are sensitive to the response of anti‐cancer drugs. The numerical results favor the Markov neighborhood regression method to the existing ones.
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spelling pubmed-94277302022-10-14 Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models Sun, Lizhe Liang, Faming Stat Med Research Articles High‐dimensional inference is one of fundamental problems in modern biomedical studies. However, the existing methods do not perform satisfactorily. Based on the Markov property of graphical models and the likelihood ratio test, this article provides a simple justification for the Markov neighborhood regression method such that it can be applied to statistical inference for high‐dimensional generalized linear models with mixed features. The Markov neighborhood regression method is highly attractive in that it breaks the high‐dimensional inference problems into a series of low‐dimensional inference problems. The proposed method is applied to the cancer cell line encyclopedia data for identification of the genes and mutations that are sensitive to the response of anti‐cancer drugs. The numerical results favor the Markov neighborhood regression method to the existing ones. John Wiley and Sons Inc. 2022-06-10 2022-09-10 /pmc/articles/PMC9427730/ /pubmed/35688606 http://dx.doi.org/10.1002/sim.9493 Text en © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Sun, Lizhe
Liang, Faming
Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
title Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
title_full Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
title_fullStr Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
title_full_unstemmed Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
title_short Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
title_sort markov neighborhood regression for statistical inference of high‐dimensional generalized linear models
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427730/
https://www.ncbi.nlm.nih.gov/pubmed/35688606
http://dx.doi.org/10.1002/sim.9493
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