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Showing 7 results for McMc
Mohammad Reza Farid Rohani, Khalil Shafiei Holighi,
Volume 1, Issue 2 (2-2008)
Abstract
In recent years, some statisticians have studied the signal detection problem by using the random field theory. In this paper we have considered point estimation of the Gaussian scale space random field parameters in the Bayesian approach. Since the posterior distribution for the parameters of interest dose not have a closed form, we introduce the Markov Chain Monte Carlo (MCMC) algorithm to approximate the Bayesian estimations. We have also applied the proposed procedure to real fMRI data, collected by the Montreal Neurological Institute.
Rasoul Garaaghaji Asl, Mohammad Reza Meshkani, Soghrat Faghihzadeh, Anoushirvan Kazemnazhad, Gholamreza Babayi, Farid Zayeri,
Volume 1, Issue 2 (2-2008)
Abstract
Modeling correlated ordinal response data is usually more complex than the case of continuous and binary responses. Existing literature lacks an appropriate approach to modeling such data. For small sample sizes, however, these models lose their appeal since their inferences are based on large samples. In this work, the Bayesian analysis of an asymmetric bivariate ordinal latent variable model has been developed. The latent response variable has been chosen to follow the generalized bivariate Gumble distribution. Using some specific priors and MCMC algorithms the regression parameters were estimated. As an application, a data set concerning Diabetic Retinopathy in 116 patients have been analyzed. This data set includes the disease status of each eye for patients as an ordinal response and a number of explanatory variables some of which are common to both eyes and the rest are organ-specific.
Atefeh Farokhy, Mousa Golalizadeh,
Volume 4, Issue 1 (9-2010)
Abstract
The multilevel models are used in applied sciences including social sciences, sociology, medicine, economic for analysing correlated data. There are various approaches to estimate the model parameters when the responses are normally distributed. To implement the Bayesian approach, a generalized version of the Markov Chain Monte Carlo algorithm, which has a simple structure and removes the correlations among the simulated samples for the fixed parameters and the errors in higher levels, is used in this article. Because the dimension of the covariance matrix for the new error vector is increased, based upon the Cholesky decomposition of the covariance matrix, two methods are proposed to speed the convergence of this approach. Then, the performances of these methods are evaluated in a simulation study and real life data.
Behrooz Kavehie, Soghrat Faghihzadeh, Farzad Eskandari, Anooshiravan Kazemnejad, Tooba Ghazanfari,
Volume 4, Issue 2 (3-2011)
Abstract
Sometimes it is impossible to directly measure the effect of intervention (medicine or therapeutic methods) in medical researches. That is because of high costs, long time, the aggressiveness of therapeutic methods, lack of clinical responses, and etc. In such cases, the effect of intervention on surrogate variables is measured. Many statistical studies have been accomplished for measuring the validity of surrogates and introducing a criterion for testing. The first criterion was established based on hypothesis testing. Other criterions were introduced over time. Then by using the classic methods, the Likelihood Ratio Factor was introduced. After that, the Bayesian Likelihood Ratio Factor developed and published. This article aims to introduce the Bayesian Likelihood Ratio Factor based on time dependent data. The illness under study is lung disease in victims of chemical weapons. The surrogate therapy method uses the forced expiratory volume at fist second.
Ebrahim Khodaie, Roohollah Shojaei,
Volume 6, Issue 1 (8-2012)
Abstract
Sampling weights are calibrated according to the theory of calibration when the sum of population total for auxiliary variables is known. Under known population, totals for auxiliary variables and some conditions Devile and Sarndal showed that generalized regression estimators could approximate calibration estimators and their variances. In this paper, under unknown population totals for auxiliary variables, an estimator for the population total is proposed and its variance is obtained. It is shown that our estimator for the population total is more efficient than the Horvitz-Thompson estimators by theoretically and simulation results.
Sedighe Eshaghi, Hossein Baghishani, Negar Eghbal,
Volume 12, Issue 1 (9-2018)
Abstract
Introducing some efficient model selection criteria for mixed models is a substantial challenge; Its source is indeed fitting the model and computing the maximum likelihood estimates of the parameters. Data cloning is a new method to fit mixed models efficiently in a likelihood-based approach. This method has been popular recently and avoids the main problems of other likelihood-based methods in mixed models. A disadvantage of data cloning is its inability of computing the maximum of likelihood function of the model. This value is a key quantity in proposing and calculating information criteria. Therefore, it seems that we can not, directly, define an appropriate information criterion by data cloning approach. In this paper, this believe is broken and a criterion based on data cloning is introduced. The performance of the proposed model selection criterion is also evaluated by a simulation study.
Bahram Haji Joudaki, Soliman Khazaei, Reza Hashemi,
Volume 19, Issue 1 (9-2025)
Abstract
Accelerated failure time models are used in survival analysis when the data is censored, especially when combined with auxiliary variables. When the models in question depend on an unknown parameter, one of the methods that can be applied is Bayesian methods, which consider the parameter space as infinitely dimensional. In this framework, the Dirichlet process mixture model plays an important role. In this paper, a Dirichlet process mixture model with the Burr XII distribution as the kernel is considered for modeling the survival distribution in the accelerated failure time. Then, MCMC methods were employed to generate samples from the posterior distribution. The performance of the proposed model is compared with the Polya tree mixture models based on simulated and real data. The results obtained show that the proposed model performs better.
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