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

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.