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Showing 6 results for Sayyareh
Abdolreza Sayyareh, Raouf Obeidi,
Volume 4, Issue 1 (9-2010)
Abstract
AIC is commonly used for model selection but the value of AIC has no direct interpretation Cox's test is a generalization of the likelihood ratio test When the true model is unknown based on AIC we select a model but we cannot talk about the closeness of the selected model to the true model Because it is not clear the selected model is wellspecified or mis-specified This paper extends Akaikes AIC-type model selection beside the Cox test for model selection and based on the simulations we study the results of AIC and Cox's test and the ability of these two criterion and test to discriminate models If based on AIC we select a model whether or not Cox's test has a ability of selecting a better model Words which one will considering the foundations of the rival models On the other hand the model selection literature has been generally poor at reflecting the foundations of a set of reasonable models when the true model is unknown As a part of results we will propose an approach to selecting the reasonable set of models
Abdolreza Sayyareh,
Volume 4, Issue 2 (3-2011)
Abstract
In this paper we have established for the Kullback-Leibler divergence that the relative error is supperadditive. It shows that a mixture of k rival models gives a better upper bound for Kullback-Leibler divergence to model selection. In fact, it is shown that the mixed model introduce a model which is better than of the all rival models in the mixture or a model which is better than the worst rival model in the mixture.
Ghobad Barmalzan, Abdolreza Sayyareh,
Volume 4, Issue 2 (3-2011)
Abstract
Suppose we have a random sample of size n of a population with true density h(.). In general, h(.) is unknown and we use the model f as an approximation of this density function. We do inference based on f. Clearly, f must be close to the true density h, to reach a valid inference about the population. The suggestion of an absolute model based on a few obsevations, as an approximation or estimation of the true density, h, results a great risk in the model selection. For this reason, we choose k non-nested models and investigate the model which is closer to the true density. In this paper, we investigate this main question in the model selection that how is it possible to gain a collection of appropriate models for the estimation of the true density function h, based on Kullback-Leibler risk.
Nasrin Moradi, Abdolreza Sayyareh, Hanieh Panahi,
Volume 8, Issue 1 (9-2014)
Abstract
In this article, the parameters of the Exponentiated Burr type III distribution have been estimated based on type II censored data using maximum likelihood method with EM algorithm and Bayesian approach under Gamma prior distributions against the squared error, linex and entropy loss functions. Importance sampling technique and Lindley's approximation method have been applied to evaluate these Bayes estimates. The results are checked by simulation study and analyzing real data of acute myelogeneous disease. The Bayes estimates are, generally, better than the MLEs and all estimates improve by increasing sample size.
Hamid Lorestani, Abdolreza Sayyareh,
Volume 9, Issue 2 (2-2016)
Abstract
Most of natural phenomena are modeled with univariate and multivariate normal distributions and their derivatives. Folded normal variables are defined as the absolute of normal random variables. So far, univariate and bivariate normal distributions, their characteristics and usages have been studied too. Distribution of the maximum of dependent random variables which have elliptically contoured distribution, has been considered by others. In this paper, distribution of the maximum of dependent random variables with bivariate folded standard normal distribution, which their joint distribution is not of the elliptically contoured family, is calculated. Also, the mean, variance and moment generating function of this distribution are investigated.
Abdolreza Sayyareh, Saeide Abdollahzadeh,
Volume 18, Issue 2 (2-2025)
Abstract
Non-invasive NIPT test has been used in trisomy 21 screening. However, there is a possibility of misdiagnosis in the methods used to diagnose Down syndrome. Therefore, it is essential to provide a process that can be used alongside these methods to improve efficiency. The main goal of this article is to design a model based on machine learning algorithms for the early diagnosis of Down syndrome. Machine learning algorithms such as support vector machine, simple Bayes, decision tree, random forest, and nearest neighbor, which are frequently used to improve the diagnosis of disorders, have been implemented on the mentioned dataset. The performance of each model on the Down syndrome dataset was investigated, and the most suitable model for this purpose was introduced.
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