Biostatistics Weekly Seminar

The utility of the epsilon-skew-normal distribution in the context of receiver operating characteristic (ROC) curve fitting

Terry Mashtare, PhD
University at Buffalo

In this talk, we extend the well-known parametric normal model via implementation of the epsilon-skew-normal (ESN) distribution developed by Mudholkar and Hutson (2000). We derive the equation for the receiver operating characteristic (ROC) curve assuming a parametric ESN model and examine the behavior of the maximum likelihood estimates for estimating the model parameters. We then summarize the results of a simulation study to examine the asymptotic properties of the maximum likelihood estimates in the parametric ESN model and compare with the maximum likelihood estimates in the normal model. We also summarize the results of a simulation study comparing the two parametric models to the nonparametric ROC model. We then illustrate the maximum likelihood estimation of the parametric ESN model using data involving skeletal measurements in 507 physically active individuals.

MRBIII, Room 1220
30 October 2019

Speaker Itinerary

Topic revision: r1 - 24 Oct 2019, TawannaPeters

This site is powered by FoswikiCopyright © 2013-2017 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback