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

Chenguang Wang, Ao Yuan, Leslie Cope, Jing Qin

Research output: Contribution to journalArticlepeer-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 languageEnglish (US)
JournalBiometrics
DOIs
StateAccepted/In press - 2021

Keywords

  • expectation-maximization algorithm
  • isotonic regression
  • maximum likelihood estimation
  • skew normal

ASJC Scopus subject areas

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

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