Abstract Number:
3110
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Tingting Zhan (1), Misung Yi (2), Inna Chervoneva (3)
Institutions:
(1) Thomas Jefferson University, N/A, (2) N/A, N/A, (3) Thomas Jefferson University, Sidney Kimmel Medical College, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
A mixture of 4-parameter Tukey's g-&-h distributions is proposed for fitting finite mixtures with Gaussian and non-Gaussian components. Since the likelihood of the Tukey's g-&-h mixtures does not have a closed analytical form, we propose a Quantile Least Mahalanobis Distance (QLMD) estimator for 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's g-&-h mixture model and implemented all proposed methods in the R package QuantileGH available CRAN. A simulation study was conducted to evaluate performance of the Tukey's g-&-h mixtures and compare to performance of mixtures of skew-normal or skew-t distributions. The Tukey's g-&-h mixtures were applied to model cellular expressions of Cyclin D1 protein in breast cancer tissues, and resulting parameter estimates evaluated as predictors of progression-free survival.
Keywords:
Finite Mixtures|Tukey’s g-&-h distribution|Indirect Estimator|Quantile Least Mahalanobis Distance|Cellular Protein Level|
Sponsors:
Section on Statistical Computing
Tracks:
Computationally Intensive Methods
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