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Showing 2 results for Khazaei
Mojtaba Khazaei,
Volume 2, Issue 1 (8-2008)
Abstract
One of the models that can be used to study the relationship between Boolean random sets and explanatory variables is growth regression model which is defined by generalization of Boolean model and permitting its grains distribution to be dependent on the values of explanatory variables. This model can be used in the study of behavior of Boolean random sets when their coverage regions variation is associated with the variation of grains size. In this paper we make possible the identification and fitting suitable growth model using available information in Boolean model realizations and values of explanatory variables. Also, a suitable method for fitting growth regression model is presented and properties of its obtained estimators are studied by a simulation study.
Bahram Haji Joudaki, Reza Hashemi, Soliman Khazaei,
Volume 17, Issue 2 (2-2024)
Abstract
In this paper, a new Dirichlet process mixture model with the generalized inverse Weibull distribution as the kernel is proposed. After determining the prior distribution of the parameters in the proposed model, Markov Chain Monte Carlo methods were applied to generate a sample from the posterior distribution of the parameters. The performance of the presented model is illustrated by analyzing real and simulated data sets, in which some data are right-censored. Another potential of the proposed model demonstrated for data clustering. Obtained results indicate the acceptable performance of the introduced model.
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