1 research outputs found
A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components
Estimating parameters of mixture model has wide applications ranging from
classification problems to estimating of complex distributions. Most of the
current literature on estimating the parameters of the mixture densities are
based on iterative Expectation Maximization (EM) type algorithms which require
the use of either taking expectations over the latent label variables or
generating samples from the conditional distribution of such latent labels
using the Bayes rule. Moreover, when the number of components is unknown, the
problem becomes computationally more demanding due to well-known label
switching issues \cite{richardson1997bayesian}. In this paper, we propose a
robust and quick approach based on change-point methods to determine the number
of mixture components that works for almost any location-scale families even
when the components are heavy tailed (e.g., Cauchy). We present several
numerical illustrations by comparing our method with some of popular methods
available in the literature using simulated data and real case studies. The
proposed method is shown be as much as 500 times faster than some of the
competing methods and are also shown to be more accurate in estimating the
mixture distributions by goodness-of-fit tests