A semiparametric isotonic regression model for skewed distributions with application to DNA-RNA-protein analysis.
expectation-maximization algorithm
isotonic regression
maximum likelihood estimation
skew normal
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
revised:
22
06
2021
received:
17
11
2019
accepted:
28
06
2021
pubmed:
8
9
2021
medline:
27
12
2022
entrez:
7
9
2021
Statut:
ppublish
Résumé
In this paper, we propose a semiparametric regression model that is built upon an isotonic regression model with the assumption that the random error follows a skewed distribution. We develop an expectation-maximization algorithm for obtaining the maximum likelihood estimates of the model parameters, examine the asymptotic properties of the estimators, conduct simulation studies to explore the performance of the proposed model, and apply the method to evaluate the DNA-RNA-protein relationship and identify genes that are key factors in tumor progression.
Substances chimiques
DNA
9007-49-2
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1464-1474Subventions
Organisme : NCI NIH HHS
ID : P30 CA006973
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA098252
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA006973
Pays : United States
Informations de copyright
© 2021 The International Biometric Society.
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