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Showing 7 results for Subject:
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, 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.
Akram Kohansal, Nafiseh Alemohammad, Fatemeh Azizzadeh,
Volume 14, Issue 2 (2-2021)
Abstract
The Bayesian estimation of the stress-strength parameter in Lomax distribution under the progressive hybrid censored sample is considered in three cases. First, assuming the stress and strength are two random variables with a common scale and different shape parameters. The Bayesian estimations of these parameters are approximated by Lindley method and the Gibbs algorithm. Second, assuming the scale parameter is known, the exact Bayes estimation of the stress-strength parameter is obtained. Third, assuming all parameters are unknown, the Bayesian estimation of the stress-strength parameter is derived via the Gibbs algorithm. Also, the maximum likelihood estimations are calculated, and the usefulness of the Bayesian estimations is confirmed, in comparison with them. Finally, the different methods are evaluated utilizing the Monte Carlo simulation and one real data set is analyzed.
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.
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.
Fateme Sadat Mirsadooghi, Akram Kohansal,
Volume 17, Issue 2 (2-2024)
Abstract
In this paper, under adaptive hybrid progressive censoring samples, Bayes estimation of the multi-component reliability, with the non-identical-component strengths, in unit generalized Gompertz distribution is considered. This problem is solved in three cases. In the first case, strengths and stress variables are assumed to have unknown, uncommon parameters. In the second case, it is assumed that strengths and stress variables have two common and one uncommon parameter, so all of these parameters are unknown. In the third case, it is assumed that strengths and stress variables have two known common parameters and one unknown uncommon parameter. In each of these cases, Bayes estimation of the multi-component reliability, with the non-identical-component strengths, is obtained with different methods. Finally, different estimations are compared using the Monte Carlo simulation, and the results are implemented on one real data set.
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