Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
2
2017
3
1
The Lomax-Exponential Distribution, Some Properties and Applications
131
153
EN
Nasrin
Hami Golzar
Masoud
Ganji
Hossein
Bevrani
10.18869/acadpub.jsri.13.2.131
Abstract: The exponential distribution is a popular model in applications to real data. We propose a new extension of this distribution, called the Lomax-exponential distribution, which presents greater flexibility to the model. Also there is a simple relation between the Lomax-exponential distribution and the Lomax distribution. Results for moment, limit behavior, hazard function, Shannon entropy and order statistic are provided. To estimate the model parameters, the method of maximum likelihood and Bayse estimations are proposed. Two data sets are used to illustrate the applicability of the Lomax-exponential distribution.
T-X family, Lomax distribution, Shannon entropy, simulation study.
http://jsri.srtc.ac.ir/article-1-237-en.html
http://jsri.srtc.ac.ir/article-1-237-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
2
2017
3
1
Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model
155
180
EN
Vahid
Tadayon
10.18869/acadpub.jsri.13.2.155
Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data.
We utilize data augmentation method and Markov chain Monte Carlo (MCMC) algorithms to do posterior calculations. The methodology is illustrated using simulated data, as well as applying it to a real data set.
Censored data, data augmentation, non-Gaussian spatial models, outlier, unified skew Gaussian.
http://jsri.srtc.ac.ir/article-1-241-en.html
http://jsri.srtc.ac.ir/article-1-241-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
2
2017
3
1
Bayesian Estimation of Parameters in the Exponentiated Gumbel Distribution
181
195
EN
Gholamhossein
Gholami
10.18869/acadpub.jsri.13.2.181
Abstract: The Exponentiated Gumbel (EG) distribution has been proposed to capture some aspects of the data that the Gumbel distribution fails to specify. In this paper, we estimate the EGchr('39')s parameters in the Bayesian framework. We consider a 2-level hierarchical structure for prior distribution. As the posterior distributions do not admit a closed form, we do an approximated inference by using Gibbs and Metropolis-Hastings algorithm.
Bayesian inference, exponentiated distributions, Gumbel distribution, Gibbs Sampler, Monte Carlo Markov Chain (MCMC) method, Metropolis-Hastings algorithm.
http://jsri.srtc.ac.ir/article-1-236-en.html
http://jsri.srtc.ac.ir/article-1-236-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
2
2017
3
1
Mixture of Normal Mean-Variance of Lindley Distributions
197
214
EN
Mehrdad
Naderi
Alireza
Arabpour
Ahad
Jamalizadeh
10.18869/acadpub.jsri.13.2.197
Abstract: In this paper, a new mixture modelling using the normal mean-variance mixture of Lindley (NMVL) distribution has been considered. The proposed model is heavy-tailed and multimodal and can be used in dealing with asymmetric data in various theoretic and applied problems. We present a feasible computationally analytical EM algorithm for computing the maximum likelihood estimates. The behavior of the obtained maximum likelihood estimators is studied with respect to bias and mean squared errors through conducting a simulation study. Two examples with flow cytometry data are used to illustrate the applicability of the proposed model.
Finite mixture model, Mean-variance mixture distribution, Lindley distribution, EM algorithm.
http://jsri.srtc.ac.ir/article-1-240-en.html
http://jsri.srtc.ac.ir/article-1-240-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
2
2017
3
1
Spatial Beta Regression Model with Random Effect
215
230
EN
Lida
Kalhori
Mohsen
Mohhamadzadeh
10.18869/acadpub.jsri.13.2.215
Abstract: In many applications we have to encountered with bounded dependent variables. Beta regression model can be used to deal with these kinds of response variables. In this paper we aim to study spatially correlated responses in the unit interval. Initially we introduce spatial beta generalized linear mixed model in which the spatial correlation is captured through a random effect. Then the performances of the proposed model is evaluated via a simulation study, implementing Bayesian approach for parameter estimation. Finally the application of this model on two real data sets about migration rate and divorce rate in Iran are presented.
Bayesian estimation, beta regression model, spatial correlation, random effect.
http://jsri.srtc.ac.ir/article-1-239-en.html
http://jsri.srtc.ac.ir/article-1-239-en.pdf
Statistical Research and Training Center - Statistical Centre of Iran
Journal of Statistical Research of Iran JSRI
1735-1294
13
2
2017
3
1
A Method to Expand Family of Continuous Distributions based on Truncated Distributions
231
247
EN
Abbas
Mahdavi
Giovana
Oliveira Silva
10.18869/acadpub.jsri.13.2.231
Abstract: A new method to generate various family of distributions is introduced. This method introduces a new two-parameter extension of the exponential distribution to illustrate its application. Some statistical and reliability properties of the new distribution, including explicit expressions for the moments, quantiles, mode, moment generating function, mean residual lifetime, stochastic orders, order statistics and some entropies are discussed. Maximum likelihood method is used to estimate the unknown parameters and the Fisher information matrix is given. The obtained results are validated using a real data set and it is shown that the new family provides a better fit than some other known distributions.
Exponential distribution, hazard rate function, truncated exponential-exponential distribution, maximum-likelihood estimation, survival function.
http://jsri.srtc.ac.ir/article-1-238-en.html
http://jsri.srtc.ac.ir/article-1-238-en.pdf