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:: Search published articles ::
Showing 7 results for Fallah

Rahman Farnoosh, Afshin Fallah, Arezoo Hajrajabi,
Volume 2, Issue 2 (2-2009)
Abstract

The modified likelihood ratio test, which is based on penalized likelihood function, is usually used for testing homogeneity of the mixture models. The efficiency of this test is seriously affected by the shape of penalty function that is used in penalized likelihood function. The selection of penalty function is usually based on avoiding of complexity and increasing tractability, hence the results may be far from optimality. In this paper, we consider a more general form of penalty function that depends on a shape parameter. Then this shape parameter and the parameters of mixture models are estimated by using Bayesian paradigm. It is shown that the proposed Bayesian approach is more efficient in comparison to modified likelihood test. The proposed Bayesian approach is clearly more efficient, specially in nonidentifiability situation, where frequentist approaches are almost failed.

Hamid Reza Chareh, Afshin Fallah,
Volume 4, Issue 2 (3-2011)
Abstract

This paper considers the weight distributions in order to incorporating the topics related to construction of skew-symetric (skew-normal) and bimodal distributions. It discusses that many of skew-normal distributions disscussed in recent years researches can be studid in more general form along with some other interesting aspects in context of weigth distributions. Two cosiderable case of the recent years reaserches have been disscussed. It is shown that the introduced distributions in these reseaches along with all of their interesting properties can be obtain from weigth distribution perspective as only special cases.

Afshin Fallah, Mahsa Nadifar, Ramin Kazemi,
Volume 7, Issue 1 (9-2013)
Abstract

In this  paper  the  regression analysis with finite mixture bivariate poisson response variable is investigated from the Bayesian point of view. It is shown that  the posterior distribution can not be written in a closed form due to the  complexity of the likelihood function of bivariate Poisson distribution. Hence, the full conditional posterior distributions of the parameters are computed and the Gibbs algorithm is used to sampling from posterior distributions. A simulation study is performed in order to assess the proposed Bayesian model and its efficiency in estimation of the parameters is compared with their frequentist counterparts. Also, a real example presented to illustrate and assess the proposed Bayesian model. The results indicate to the more efficiency of the  estimators resulted from Bayesian  approach than estimators of frequentist approach at least for small sample sizes.

Afshin Fallah, Ramin Kazemi, Hasan Khosravi,
Volume 11, Issue 2 (3-2018)
Abstract

Regression analysis is done, traditionally, considering homogeneity and normality assumption for the response variable distribution. Whereas in many applications, observations indicate to a heterogeneous structure containing some sub-populations with skew-symmetric structure either due to heterogeneity, multimodality or skewness of the population or a combination of them. In this situations, one can use a mixture of skew-symmetric distributions to model the population. In this paper we considered the Bayesian approach of regression analysis under the assumption of heterogeneity of population and a skew-symmetric distribution for sub-populations, by using a mixture of skew normal distributions. We used a simulation study and a real world example to assess the proposed Bayesian methodology and to compare it with frequentist approach.

Zahra Rahimian Azad, Afshin Fallah,
Volume 15, Issue 1 (9-2021)
Abstract

This paper considers the Bayesian model averaging of inverse Gaussian regression models for regression analysis in situations that the response observations are positive and right-skewed. The computational challenges related to computing the essential quantities for executing of this methodology and their dominating ways are discussed. Providing closed form expressions for the interested posterior quantities by considering suitable prior distributions is an attractive aspect of the proposed methodology. The proposed approach has been evaluated via a simulation study and its applicability is expressed by using a real example related to the seismic studies. 

Mahsa Nadifar, Hossein Baghishani, Afshin Fallah,
Volume 15, Issue 1 (9-2021)
Abstract

Many of spatial-temporal data, particularly in medicine and disease mapping, are counts. Typically, these types of count data have extra variability that distrusts the classical Poisson model's performance. Therefore, incorporating this variability into the modeling process, plays an essential role in improving the efficiency of spatial-temporal data analysis. For this purpose, in this paper, a new Bayesian spatial-temporal model, called gamma count, with enough flexibility in modeling dispersion is introduced. For implementing statistical inference in the proposed model, the integrated nested Laplace approximation method is applied. A simulation study was performed to evaluate the performance of the proposed model compared to the traditional models. In addition, the application of the model has been demonstrated in analyzing leukemia data in Khorasan Razavi province, Iran.

Lida Kalhori Nadrabadi, Zohreh Fallah Mohsekhani,
Volume 16, Issue 1 (9-2022)
Abstract

In countries where labor force surveys are based on rotation samples and partially standard sample units at different periods, the number of changing statuses can be estimated and presented as flow statistics. The response error is one of the essential non-sampling errors in labor force statistics. This error is doubled in flow statistics. Usually, the error of classifying flow statistics is estimated using the interview method, which is costly and complex. This paper presents the process of estimating flow statistics and appropriate models for calculating the classification error for it. Also, according to Iran's sample rotation pattern, each model's feasibility is examined. Finally, the Markov latent class model, assuming inequality of transition probabilities based on the rotation pattern of Iran for labor force samples, is introduced as a fit model for estimating classification error for flow statistics in Iran using the labour force survey data of 2019 and 2020.


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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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