A semiparametric isotonic regression model for skewed distributions with application to DNA–RNA–protein analysis

Chenguang Wang; Ao Yuan; Leslie Cope; Jing Qin

doi:10.1111/biom.13528

A semiparametric isotonic regression model for skewed distributions with application to DNA–RNA–protein analysis

Chenguang Wang, Ao Yuan, Leslie Cope, Jing Qin

School of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English (US)
Pages (from-to)	1464-1474
Number of pages	11
Journal	Biometrics
Volume	78
Issue number	4
DOIs	https://doi.org/10.1111/biom.13528
State	Published - Dec 2022

Keywords

expectation-maximization algorithm
isotonic regression
maximum likelihood estimation
skew normal

ASJC Scopus subject areas

Agricultural and Biological Sciences(all)
Applied Mathematics
Biochemistry, Genetics and Molecular Biology(all)
Immunology and Microbiology(all)
Statistics and Probability

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10.1111/biom.13528

Cite this

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title = "A semiparametric isotonic regression model for skewed distributions with application to DNA–RNA–protein analysis",

abstract = "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.",

keywords = "expectation-maximization algorithm, isotonic regression, maximum likelihood estimation, skew normal",

author = "Chenguang Wang and Ao Yuan and Leslie Cope and Jing Qin",

note = "Funding Information: The authors gratefully acknowledge partial support from the following sources: CW, NCI P30CA006973, NCI 1 P50 CA098252; LC, NCI P30CA006973. The authors thank the editors and referees for their helpful comments that substantially strengthened this article. Publisher Copyright: {\textcopyright} 2021 The International Biometric Society.",

year = "2022",

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AU - Wang, Chenguang

AU - Yuan, Ao

AU - Cope, Leslie

AU - Qin, Jing

N1 - Funding Information: The authors gratefully acknowledge partial support from the following sources: CW, NCI P30CA006973, NCI 1 P50 CA098252; LC, NCI P30CA006973. The authors thank the editors and referees for their helpful comments that substantially strengthened this article. Publisher Copyright: © 2021 The International Biometric Society.

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

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KW - maximum likelihood estimation

KW - skew normal

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A semiparametric isotonic regression model for skewed distributions with application to DNA–RNA–protein analysis

Abstract

Keywords

ASJC Scopus subject areas

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