Characterizing Components in a Mixture Model for Birthweight Distribution

Screening for a Multivariate Mixture Normal Distribution

Determination of the number of components in finite mixture distribution with Skew-t-Normal components

Abstract One of the main goal in the mixture distributions is to determine the number of components. There are different methods for determination the number of components, for example, Greedy-EM algorithm which is based on adding a new component to the model until satisfied the best number of components. The second method is based on maximum entropy and finally the third method is based on non...

Testing for a Finite Mixture Model with Two Components

We consider a finite mixture model with k components and a kernel distribution from a general parametric family. We consider the problem of testing the hypothesis k = 2 against k ≥ 3. In this problem, the likelihood ratio test has a very complicated large sample theory and is difficult to use in practice. We propose a test based on the likelihood ratio statistic where the estimates of the param...

Characterizing wood fiber and particle length with a mixture distribution and a segmented distribution

The length data from 12 samples of wood fibers and particles were described using lognormal and Weibull distributions. While both distributions fitted the middle range of the data well, the lognormal distribution provided a closer fit for short fibers and particles and the Weibull distribution was more appropriate for long ones. A mixture of the lognormal and Weibull distributions was developed...

screening for a multivariate mixture normal distribution