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Showing 116 results for Type of Study: Applied
Gholam Hossein Shahkar, Rahim Badamchizadeh,
Volume 1, Issue 1 (9-2007)
Abstract
In this paper we consider a single server queue with two phase arrival and two phase services. Arrival are Poison variables with different rates. For each input, the server provides private service with exponential distribution. The rates of services are different. The policy of service is FCFS, where the server changes the king of service according to the customer in the front of queue. After the completion of each service, the server either goes for a vacation with probability (1-theta), or may continue to server the next customer with probability theta, if any. Otherwise, it remains in the system until a customer arrives. Vacation times are assumed to have exponential distribution. We obtain steady-state probability generating function for queue size distribution for each input and expected busy period.
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.
Hadi Alizadeh Noughabi, Reza Alizadeh Noughabi,
Volume 2, Issue 1 (8-2008)
Abstract
In this paper we evaluate the power of the sample entropy goodness-of-fit tests for normal, exponential and uniform distributions and we compare them with the other statistical tests. We show, by simulation, that them have less power than of the other tests considered. We next introduce a new test for symmetry based on sample entropy and show, by simulation, that it has higher power than Cabilio and Masaro test (1996).
Shohre Jalaei, Soghrat Faghihzadeh, Farzad Eskandari, Touba Ghazanfari,
Volume 2, Issue 1 (8-2008)
Abstract
Part of the recent literature on the evaluation of surrogate endpoints is started by a definition of validity in terms of both trial-level and individual-level association between a potential surrogate and a true endpoint. In another part, we review the main considerable statistical methods being proposed for the evaluation of a biomarker as surrogate endpoints, which have developed and consider how the validation process might be arranged within the regulatory and practical constraints evaluation. In the present work, we propose a new. Bayesian approach to evaluate individual level surrogacy. Deferent variations to prior distributions were implemented for responses with binomial distribution. Then these methods are compared in a simulation study. Finally, we apply and compare the previous and new methodology using a clinical study.
Maryam Torkzadeh, Soroush Alimoradi,
Volume 3, Issue 1 (9-2009)
Abstract
One of the tools for determining nonlinear effects and interactions between the explanatory variables in a logistic regression model is using of evolutionary product unit neural networks. To estimate the model parameters constructed by this method, a combination of evolutionary algorithms and classical optimization tools is used. In this paper, we change the structure of neural networks in the form that all model parameters can be estimated by using an evolutionary algorithms causes a model that is Akaike information criterion is better than conventional logisti model Akaike information criterion, but using the combination method gives the best model.
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.
Hamidreza Mostafaei, Maryam Safaei,
Volume 3, Issue 2 (3-2010)
Abstract
In 2002 the enforcement on policy unification of exchange rate caused dramatic decrease in the nominal price of Iran's Rial against U.S.dollar per on unit.For this reason due to the existence of unexpected and large change we cannot use the linear time series models for surveying the fluctuations of the rate of Iran's Rial change against U.S. dollar per on unit. In this paper we compare Self-Exciting threshold autoregressive and Markov switching autoregressive model. then it will be show that only the Markov switching autoregressive model being able to show the behaviors of Iran's exchange rate.
Nasrollah Iranpanah,
Volume 3, Issue 2 (3-2010)
Abstract
Abstract: In many environmental studies, the collected data are usually spatially dependent. Determination of the spatial correlation structure of the data and prediction are two important problem in statistical analysis of spatial data. To do so, often, a parametric variogram model is fitted to the empirical variogram of the data by estimating the unknown parameters of the mentioned variogram. Since there are no closed formulas for the variogram parameters estimator, they are usually computed numerically. Therefore, the precision measures of the variogram parameters estimator and spatial prediction can be calculated using bootstrap methods. Lahiri (2003) proposed the moving block bootstrap method for spatial data, in which observations are divided into several moving blocks and resampling is done from them. Since, in this method, the presence of boundary observations in the resampling blocks have less selection chance than the other observations, therefore, the estimator of the precision measures would be biased. In this paper, revising the moving block bootstrap method, the separate block bootstrap method was presented for estimating the precision measures of the variogram parameters estimator and spatial prediction. Then its usage was illustrated in an applied 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.
Reza Hashemi, Ghobad Barmalzan, Abedin Haidari,
Volume 3, Issue 2 (3-2010)
Abstract
Considering the characteristics of the bivariate normal distribution, in which uncorrelation of two random variables is equivalent to their independence, it is interesting to verify this issue in other distributions in other words whether or not the multivariate normal distribution is the only distribution in which uncorrelation is equivalent to independence. This paper aims to answer this question by presenting some concepts and introduce another family in which uncorrelation is equivalent to independence.
Dr Shahram Mansoury, Dr Eynollah Pasha,
Volume 3, Issue 2 (3-2010)
Abstract
Stochastically ordered random variables with given marginal distributions are combined into a joint distribution preserving the ordering and the marginals using a maximum entropy principle. A closed-form of the maximum entropy density function is obtained. Next we have compared the entropies of maximum entropy distributions, under two constraints The constraints are either prescription of marginal distributions and the marginals and covariance matrix.
Hamidreza Navvabpour, Mohadese Safakish,
Volume 4, Issue 1 (9-2010)
Abstract
Parallel to social developments and increasing use of statistics in planning, demand for producing and disseminating social and economic information have been risen dramatically. Survey sampling is one of the methods of gathering such information. The quality of survey data is important for planners and researches. In recent years, the concept of response burden has been expressed as one of the new components of the data quality. Unfortunately, In National Statistical System of Iran nothing has been done to evaluate response burden in order to improve survey data quality. In this paper, we introduce existing methods of measuring response burden and express other countries experiences to assess perceived response burden in surveys. A common approach to assess response burden is designing a Perceived Response Burden survey and carry it out along with the host survey. In order to illustrate how to design, conduct and analyze results of this survey, a Perceived Response Burden survey which has been conducted in Faculty of Economic, Allameh Tabatabaei University, to measure response burden impose on respondents in Health Survey is presented.
Ghadi Mahdavi, Zahra Majedi,
Volume 4, Issue 1 (9-2010)
Abstract
The GARCH(1,1) and GARCH(1,1)-t models lead to highly volatile quantile forecasts, while historical simulation, Variance–Covariance, adaptive generalized Pareto distribution and non-adaptive generalized Pareto distribution models provide more stable quantile forecasts. In general, GARCH(1,1)-t, generalized Pareto distribution models and historical simulation are preferable for most quantiles.
Mehrdad Niaparast, Sahar Mehr-Mansour,
Volume 4, Issue 1 (9-2010)
Abstract
The main part of optimal designs in the mixed effects models concentrates on linear models and binary models. Recently, Poisson models with random effects have been considered by some researchers. In this paper, an especial case of the mixed effects Poisson model, namely Poisson regression with random intercept is considered. Experimental design variations are obtained in terms of the random effect variance and indicated that the variations depend on the variance parameter. Using D-efficiency criterion, the impression of random effect on the experimental setting points is studied. These points are compared with the optimal experimental setting points in the corresponding model without random effect. We indicate that the D-efficiency depends on the variance of random effect.
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.
Narges Najafi, Hossein Bevrani,
Volume 4, Issue 1 (9-2010)
Abstract
This paper is devoted to compute the sample size for estimation of Normal distribution mean with Bayesian approach. The Quadratic loss function is considered and three criterions are applied to obtain p- tolerance regions with the lowest posterior loss. These criterions are: average length, average coverage and worst outcome. The proposed methodology is examined, and its effectiveness is shown.
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.
Maryam Safaei,
Volume 5, Issue 1 (9-2011)
Abstract
This paper offers a method of the estimation of the transition probability for the behaviors of financial time series by Markov Switching Autoregressive model. Using this model, the behaviors of fluctuations of exchange rate form two regimes low and high changes rate are considered. Results of prediction show that the persistence probability of regimes will be decreased. Thus, the probability of transition to other regime will be increased if process were in a specific regime.
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