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Showing 81 results for Mohammad
Hossein Baghishani, Mohammad Mahdi Tabatabaei,
Volume 1, Issue 1 (9-2007)
Abstract
In parameter driven models, the main problem is likelihood approximation and also parameter estimation. One approach to this problem is to apply simpler likelihoods such as composite likelihood. In this paper, we first introduce the parameter driven models and composite likelihood and then define a new model selection criterion based on composite likelihood. Finally, we demonstrate composite likelihood's capabilities in inferences and accurate model selection in parameter driven models throughout a simulation study.
Firouzeh Rivaz, Mohsen Mohammadzadeh, Majid Jafari Khaledi,
Volume 1, Issue 1 (9-2007)
Abstract
In Bayesian prediction of a Gaussian space-time model, unknown parameters are considered as random variables with known prior distributions and, then the posterior and Bayesian predictive distributions are approximated with discritization method. Since prior distributions are often unknown, in this paper, parametric priors are considered. Then the empirical Bayes approach is used to estimate the prior distributions. Replacing these estimates in the Bayesian predictive distribution, an empirical Bayes space-time predictor and prediction variance are determined. Then an environmental example is used to illustrate the application of the proposed method. Finally the accuracy of the empirical Bayes space-time predictor is considered with cross validation criterion.
Mohammad Reza Alavi, Rahim Chinipardaz,
Volume 1, Issue 1 (9-2007)
Abstract
The classical analysis is based on random samples. However, in many situations the observations are recorded according to a nonnegative function of observations. In this case the mechanism of sampling is called weighted sampling. The usual statistical methods based on a weighted sample may be not valid and have to be adjusted. In this paper adjusted methods under some particular weight functions for normal distribution are studied and a new distribution called double normal distribution, is introduced as a weighted normal distribution.
Mohammad Arashi, Mahammad Mahdi Tabatabaei,
Volume 1, Issue 2 (2-2008)
Abstract
In this paper, we obtain the generalized least square, restricted generalized least square and shrinkage estimators for the regression vector parameter assuming that the errors have multivariate t distribution. Also we calculate their quadratic risks and propose the dominance order of the underlying estimators.
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.
Mohammad Ghasem Vahidi Asl, Abdollah Hasani Jalilian,
Volume 1, Issue 2 (2-2008)
Abstract
In this paper, first spatial point processes and their characteristics are briefly introduced. Then after defining the spatial Cox processes in general terms, a special subclass that is shot noise Cox processes, are investigated. Finally a Thomas process is fitted to the locations of Zagros earthquakes.
Mahmodreza Gohari, Mahmoud Mahmoudi, Kazem Mohammad, Ein Allah Pasha,
Volume 1, Issue 2 (2-2008)
Abstract
Recurrent events are one type of multivariate survival data. Correlation between observations on each subject is the most important feature of this type of data. This feature does not allow using the ordinary survival models. Frailty models are one of the main approaches to the analysis of recurrent events. Ordinary Frailty models assumed the frailty is constant over time, that is not realistic in many applications. In this paper we introduce a time-dependent frailty model. The introduced model is based on piecewise semiparametric proportional hazard and frailty variable followed a Gamma distribution. The frailty variable in the model has a gamma process that is constant during each interval and has independent increments in the beginning of each interval. We found a close form function for integrated likelihood function and estimated parameters of model. The efficiency of introduced model was compared with an ordinary constant gamma model by a simulation study
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.
Mehdi Akbarzadeh, Hamid Alavimajd, Yadollah Mehrabi, Maryam Daneshpoor, Anvar Mohammadi,
Volume 3, Issue 2 (3-2010)
Abstract
One of the important problems that bring up in genetic fields is determining of loci of special gene in order to gene mapping and generating more effective drugs in medicine. Genetic linkage analysis is one important stage in this way. Haseman-Elston method is a quantitative statistical method that is used by biostatisticians and geneticists for genetic linkage analysis. The original Haseman-Elston method is presented in the year 1972 and ever after many investigators recommended some suggestions to make better it. In this article, we introduce the Haseman-Elston regression method and its extensions through 1972 to 2009. and finally we show performance of these methods in a practical example.
Mohammadvali Ahmadi, Majid Sarmad,
Volume 3, Issue 2 (3-2010)
Abstract
Because of importance and popularity of the Normal distribution, the samples based on this distribution has been considered and the outliers are identified using cut-off values which are dependent on the sample size. A decision problem has been structured to obtain the optimal cut-off value. The problem is solved by a simulation study with a minimax rule.
Maliheh Abbasnejad Mashhadi, Davood Mohammadi,
Volume 4, Issue 1 (9-2010)
Abstract
In this paper, we characterize symmetric distributions based on Renyi entropy of order statistics in subsamples. A test of symmetry is proposed based on the estimated Renyi entropy. Critical values of the test are computed by Monte Carlo simulation. Also we compute the power of the test under different alternatives and show that it behaves better that the test of Habibi and Arghami (1386).
Mohammad Hossein Aalamatsaz, Foroogh Mahpishanian,
Volume 5, Issue 1 (9-2011)
Abstract
There is a family of generalized Farlie-Gumbel-Morgenstern copulas, known as the semiparametric family, which is generated by a function called distribution-based generator. These generators have been studied typically for symmetric distributions in the literature. In this article, is proposed a method for asymmetric case which increases the flexibility of distribution-based generators and, thus, the model. In addition, a method for generalizing general generators is provided which can also be used to obtain more flexible distribution-based generators. Clearly, with more flexible generators more desirable models can be found to fit real data.
Aref Khanjari Idenak, Mohammadreza Zadkarami, Alireza Daneshkhah,
Volume 5, Issue 2 (2-2012)
Abstract
In this paper a new compounding distribution with increasing, decreasing, bathtub shaped and unimodal-bathtub shaped hazard rate function. The new three-parameters distribution as a generalization of the exponential power distribution is proposed. Maximum likelihood estimation of the parameters, raw-moments, density function of the order statistics, survival function, hazard rate function, mean residual lifetime, reliability function and median are presented. Then the properties of this distribution are illustrated based on a real data set.
Samane Khosravi, Mohammad Amini, Gholamreza Mohtashami Borzadaran,
Volume 6, Issue 1 (8-2012)
Abstract
This paper explores the optimal criterion for comparison of some Phi-divergence measures. The dependence for generalized Farlie Gumbel Morgenstern family of copulas is numerically calculated and it has been shown that the Hellinger measure is the optimal criterion for measuring the divergence from independence.
Mohammad Gholami Fesharaki, Anoshirvan Kazemnejad, Farid Zayeri,
Volume 6, Issue 1 (8-2012)
Abstract
Skew Normal distribution is important in analyzing non-normal data. The probability density function of skew Normal distribution contains integral function which tends researchers to some problems. Because of this problem, in this paper a simpler Bayesian approach using conditioning method is proposed to estimate the parameters of skew Normal distribution. Then the accuracy of this metrology is compared with ordinary Bayesian method in a simulation study.
Mohammad Amini, Hadi Jabbari Noughabi, Mahla Ghasemnejad Farsangi,
Volume 6, Issue 2 (2-2013)
Abstract
In this paper, three new non-parametric estimator for upper tail dependence measure are introduced and it is shown that these estimators are consistent and asymptotically unbiased. Also these estimators are compared using the Mont Carlo simulation of three different copulas and present a new method in order to select the best estimator by applying the real data.
Arezou Mojiri, Soroush Alimoradi, Mohammadreza Ahmadzade,
Volume 7, Issue 1 (9-2013)
Abstract
Logistic regression models in classification problems by assuming the linear effects of covariates is a modeling for class membership posterior probabilities. The main problem that includes nonlinear combinations of covariates is maximum likelihood estimation (MLE) of the model parameters. In recent investigations, an approach of solving this problem is combination of neural networks, evolutionary algorithms and MLE methods. In this paper, another type of radial basis functions, namely inverse multiquadratic functions and hybrid method, are considered for estimating the parameters of these models. The experimental results of comparing the proposed models show that the inverse multiquadratic functions compared to the Gaussian functions have better precision in classification problems.
Kobra Gholizadeh, Mohsen Mohammadzadeh, Zahra Ghayyomi,
Volume 7, Issue 1 (9-2013)
Abstract
In Bayesian analysis of structured additive regression models which are a flexible class of statistical models, the posterior distributions are not available in a closed form, so Markov chain Monte Carlo algorithm due to complexity and large number of hyperparameters takes long time. Integrated nested Laplace approximation method can avoid the hard simulations using the Gaussian and Laplace approximations. In this paper, consideration of spatial correlation of the data in structured additive regression model and its estimation by the integrated nested Laplace approximation are studied. Then a crime data set in Tehran city are modeled and evaluated. Next, a simulation study is performed to compare the computational time and precision of the models provided by the integrated nested Laplace approximation and Markov chain Monte Carlo algorithm
Mohammad Gholami Fesharaki, Anoshirvan Kazemnejad, Farid Zayeri,
Volume 7, Issue 2 (3-2014)
Abstract
In two level modeling, random effect and error's normality assumption is one of the basic assumptions. Violating this assumption leads to incorrect inference about coefficients of the model. In this paper, to resolve this problem, we use skew normal distribution instead of normal distribution for random and error components. Also, we show that ignoring positive (negative) skewness in the model causes overestimating (underestimating) in intercept estimation and underestimating (overestimating) in slope estimation by a simulation study. Finally, we use this model to study relationship between shift work and blood cholesterol.
Hamid Karamikabir, Mohammad Arashi,
Volume 8, Issue 1 (9-2014)
Abstract
In this paper we consider of location parameter estimation in the multivariate normal distribution with unknown covariance. Two restrictions on the mean vector parameter are imposed. First we assume that all elements of mean vector are nonnegative, at the second hand assumed only a subset of elements are nonnegative. We propose a class of shrinkage estimators which dominate the minimax estimator of mean vector under the quadratic loss function.
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