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

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  • Chenguang Wang
  • Ao Yuan
  • Leslie Cope
  • Jing Qin

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.

Suggested Citation

  • Chenguang Wang & Ao Yuan & Leslie Cope & Jing Qin, 2022. "A semiparametric isotonic regression model for skewed distributions with application to DNA–RNA–protein analysis," Biometrics, The International Biometric Society, vol. 78(4), pages 1464-1474, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1464-1474
    DOI: 10.1111/biom.13528
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    References listed on IDEAS

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    4. Banerjee, Moulinath & Mukherjee, Debasri & Mishra, Santosh, 2009. "Semiparametric binary regression models under shape constraints with an application to Indian schooling data," Journal of Econometrics, Elsevier, vol. 149(2), pages 101-117, April.
    5. David Hunter & Derek Young, 2012. "Semiparametric mixtures of regressions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 19-38.
    6. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
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