A0217
Title: R package QuantileGH: Quantile least Mahalanobis distance estimator for Tukey g-\&-h mixture
Authors: Tingting Zhan - Thomas Jefferson University (United States) [presenting]
Misung Yi - Dankook University (Korea, South)
Inna Chervoneva - Thomas Jefferson University (United States)
Abstract: A mixture of 4-parameter Tukey g-\&-h distributions is proposed for fitting finite mixtures with Gaussian and non-Gaussian components. Since the likelihood of Tukey's g-\&-h mixtures does not have a closed analytical form, we propose a Quantile Least Mahalanobis Distance (QLMD) estimator for the parameters of such mixtures. QLMD is an indirect estimator minimizing the Mahalanobis distance between the sample and model-based quantiles, and its asymptotic properties follow from the general theory of indirect estimation. We have developed a stepwise algorithm to select a parsimonious Tukey g-\&-h mixture model and implemented all proposed methods in the R package QuantileGH available CRAN. A simulation study was conducted to evaluate the performance of the Tukey g-\&-h mixtures and compare them to the performance of mixtures of skew-normal or skew-t distributions. The Tukey g-\&-h mixtures were applied to model cellular expressions of Cyclin D1 protein in breast cancer tissues, and resulting parameter estimates were evaluated as predictors of progression-free survival.
