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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9427730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sunlizhe markovneighborhoodregressionforstatisticalinferenceofhighdimensionalgeneralizedlinearmodels AT liangfaming markovneighborhoodregressionforstatisticalinferenceofhighdimensionalgeneralizedlinearmodels |