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Showing 123 results for Type of Study: Applied
Masoud Ajami, Vaheed Fakoor, Sara Jomhoori,
Volume 5, Issue 1 (9-2011)
Abstract
In sampling, arisen data with probability proportional to its length is called Length-bised. Nonparametric density estimation in length-biased sampling is more difficult than other states. One of the famous estimators in this context is the one introduced by Jones (1991). In this paper, we calculate the bandwidth parameter of this estimator by Bayes'method. The strong consistency of this estimator have been proved with a random Bandwidth. We have compared the performance of Bayes'method with cross validation by using simulation studies.
Mojdeh Esmailzadeh, Farzad Eskandari, Sima Naghizadeh Ardabili,
Volume 5, Issue 2 (2-2012)
Abstract
Forecasting the future status for underlying systems or random process, is one of the most important problems. In such situations, in addition to variables, the parameters may vary during the time and hence, the independence assumption between variables and parameters is broken. For analyzing this systems, usually the dynamic generalized linear models are used based on Markov chain Monte Carlo algorithm. The purpose of this paper is applying the Bayesian dynamic generalized linear models in non-conjugate discrete structures. First, the concepts of dynamic generalized linear models are reviewed. Then, the Bayesian modeling of non-conjugated discrete structures using MCMC algorithm is studied. Finally, using the investigated model the real data set related to the economic activity condition in three provinces of Iran during the years 2006-2008 are analysed.
Mahboobeh Doosti Irani, Saeid Pooladsaz,
Volume 5, Issue 2 (2-2012)
Abstract
Consider an experimental situation where it is desired to compare more than one test treatments with a control treatment. In this paper a method is presented for achieving E-optimal incomplete block design for this situation under the assumption that the observations within each block are correlated. Then an algorithm is provided for making optimal design based on above-mentioned method. This algorithm for any correlation structure with negative non-diagonal elements is applicable.
Hamid Reza Rasouli,
Volume 5, Issue 2 (2-2012)
Abstract
In this paper the different types of autoregressive models were described for analysis of spatial data. Then the model parameters were estimated using maximum profile likelihood function by assuming that the dependent variables or error terms have spatially autoregressive relationship. Next all the models were evaluated and finally, the application of the model is illustrated in a real example.
Amal Saki Malehi, Ebrahim Hajizadeh, Kambiz Ahmadi,
Volume 6, Issue 1 (8-2012)
Abstract
The survival analysis methods are usually conducted based on assumption that the population is homogeneity. However, generally, this assumption in most cases is unrealistic, because of unobserved risk factors or subject specific random effect. Disregarding the heterogeneity leads to unbiased results. So frailty model as a mixed model was used to adjust for uncertainty that cannot be explained by observed factors in survival analysis. In this paper, family of power variance function distributions that includes gamma and inverse Gaussian distribution were introduced and evaluated for frailty effects. Finally the proportional hazard frailty models with Weibull baseline hazard as a parametric model used for analyzing survival data of the colorectal cancer patients.
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.
Sayedeh Fatemeh Miri, Ehsan Bahrami Samani,
Volume 6, Issue 1 (8-2012)
Abstract
In this paper a general model is proposed for the joint distribution of nominal, ordinal and continuous variables with and without missing data. Closed forms are presented for likelihood functions of general location models. Also the Joe approximation is used for the parameters of general location models with mixed continuous, ordinal and nominal data with non-ignorable missing responses. To explain the ability of proposed models some simulation studies are performed and some real data are analyzed from a foreign language achievement study.
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.
Abdollah Safari, Ali Sharifi, Hamid Pezeshk, Peyman Nickchi, Sayed-Amir Marashi, Changiz Eslahchi,
Volume 6, Issue 2 (2-2013)
Abstract
There are several methods for inference about gene networks, but there are few cases in which the historical information have been considered. In this research we deal with Bayesian inference on gene network. We apply a Bayesian framework to use the available information. Assuming a proper prior distribution and taking the dependency of parameters into account, we seek a model to obtain promising results. We also deal with the hyper parameter estimation. Two methods are considered. The results will be compared by the use of a simulation based on Gibbs sampler. The strengths and weaknesses of each method are briefly mentioned.
Mohamad Bayat, Jafar Ahmadi,
Volume 6, Issue 2 (2-2013)
Abstract
Nowadays, the use of various types of censoring plan in studies of lifetime engineering systems and industrial experiment are worthwhile. In this paper, by using the idea in Cramer and Iliopoulos (2010), an adaptive progressive Type-I censoring is introduced. It is assumed that the next censoring number is random variable and depends on the previous censoring numbers, previous failure times and censoring times. General distributional results are obtained in explicit analytic forms. It is shown that maximum likelihood estimators coincide with those in deterministic progressive Type-I censoring. Finally, in order to illustrate and make a comparison, simulation study is done for one-parameter exponential distribution.
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
Gholam Ali Parham, Parisa Masjedi,
Volume 7, Issue 2 (3-2014)
Abstract
One of the issues in reviewing the performance of a financial market is existence of long-term memory. Since for a financial time series, we may find this feature in the volatility. So reviewing in volatility has been considered by many economists. A common method for identification and modeling of long-term memory in the volatility is to use FIGARCH models. In this paper, we identify and model long-term memory in the data exchange rates volatility (EUR/IRR). According to the statistical properties of skewness, heavy tail and excess kurtosis of data, assuming normal residuals being rejected and therefore cannot identify model by using common methods. The data structure looks NIG distribution is a good choice for the distribution of residuals. Hence with this assumption, we again identify model. The results show a good selection for data is FIGARCH-NIG model.
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.
Sedighe Zamani Mehryan, Ali Reza Nematollahi,
Volume 7, Issue 2 (3-2014)
Abstract
In this paper, the pseudo-likelihood estimators and the limiting distribution of the score test statistic associated with several hypothesis tests such as unit root test for the linear regression models with stationary and nonstationary residuals are calculated. The limiting behavior of theses test statistics by using a simpler approach of the original presentation is derived. Also by using a Mont Carlo method, it is shown that the derived pseudo-likelihood estimators are appropriate. The quantiles of the limiting distribution of the test statistic for a unit root are also calculated and a new table is provided which can be used by researchers for the unit root test.
Ali Sharifi, Seyedreza Hashemi,
Volume 8, Issue 1 (9-2014)
Abstract
A semiparametric additive-multiplicative intensity function for recurrent events data under two competing risks have been supposed in this paper. The model contains unknown baseline hazard function that defined separately intensity function for different competing risks effects on subjects failure. The presented model is based on regression parameters for effective covariates and frailty variable which describe correlation between terminal event and recurrent events and personal difference of under study subjects. The model support right censored and informative censored survival data. For estimating unknown parameters, numerical methods have been used and baseline hazard parameters are approximated using Taylor series expansion. A simulation study and application of the model to the bone marrow transplantation data are performed to illustrate the performance of the proposed model.
Anahita Nodehi, Mousa Golalizadeh,
Volume 8, Issue 1 (9-2014)
Abstract
Bivariate Von Mises distribution, which behaves relatively similar to bivariate normal distributions, has been proposed for representing the simultaneously probabilistic variability of these angles. One of the remarkable properties of this distribution is having the univariate Von Mises as the conditional density. However, the marginal density takes various structures depend on its involved parameters and, in general, has no closed form. This issue encounters the statistical inference with particular problems. In this paper, this distribution and its properties are studied, then the procedure to sample via the acceptance-rejection algorithm is described. The problems encountered in choosing a proper candidate distribution, arising from the cyclic feature of both angles, is investigated and the properties of its conditional density is utilized to overcome this obstacle.
Forough Hajibagheri, Abdolrahman Rasekh, Mohammad Reza Akhoond,
Volume 8, Issue 1 (9-2014)
Abstract
The instability of the least squares parameter estimates under collinearity, might also causes instability of the residuals. If so, a large residual from a least squares fit might not be indicative of an erratic data point, and conversely. In order to resolve the problem of collinearity in the regression model, biased estimators like the Liu estimator is suggested. In this paper, it is shown that when Liu mean shift regression is used to mitigate the effect of the collinearity, the influence of some observations can be drastically changed and also the appropriate statistic for testing outliers is derived. In order to illustrate the performance of the proposed method, a real example is presented.
Ehsan Kharati Koopaei, Soltan Mohammad Sadooghi Alvandi,
Volume 8, Issue 1 (9-2014)
Abstract
The coefficient of variation is often used for comparing the dispersions of populations that have different measurement systems. In this study, the problem of testing the equality of coefficients of variation of several Normal populations is considered and a new test procedure based on Wald test and parametric bootstrap approach is developed. Since all the proposed tests for this problem are approximate, it is important to investigate how well each test controls the type I error rate. Therefore, via a simulation study, first the type I error rate of our new test is compared with some recently proposed tests. Then, the power of our proposed test is compared with others.
Sahar Mehrmansour, Mehrdad Niaparast,
Volume 8, Issue 2 (3-2015)
Abstract
The main researches of optimum experimental designs for mixed effects have been concentrated on locally optimal designs. These designs are obtained based on the initial guess of parameters. Therefore, locally designs may be the best design but for wrong assumed model. Recently, Bayesian approach has been considered by researches when information about model parameters is available. In the present work, optimal design for the mixed effects Poisson regression model based on some prior distributions are considered and for two special cases of this models the Bayesian D-optimal designs are obtained for some representative values of variance of random effect. The results are compared to Poisson regression model without random effects.
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