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Showing 4 results for Naderi
Maryam Parsaeian, Sima Naghizadeh, Habib Naderi,
Volume 23, Issue 1 (9-2018)
Abstract
Explaining the problem. The equating process is used to compare the scores of the two different tests with the same theme. The goal of this research is finding the best method of equating data using Logistic model.
Method. we are using the data of Ph.D. test in Statistic major for two consecutive years 92 and 93. For analyzing, we are specifically using the tests of Statistics major which includes 45 questions. Parameters of test and ability of individuals are considered according to the three parametrs model and by using the MULTILOG software. In this study, we are using the Mean-mean, Mean-Sigma, Haebara, and Stocking-Lord methods by considering the unequal groups with Anchor-test design, and we are using the root mean square error for choosing the optimal solution.
Conclusion. The results of this study show that the methods under characteristic curve are more accurate.
Mohammad Mollanoori, Habib Naderi, Hamed Ahmadzadeh, Salman Izadkhah,
Volume 25, Issue 1 (1-2021)
Abstract
Many populations encountered in survival analysis are often not homogeneous. Individuals are flexible in their susceptibility to causes of death, response to treatment, and influence of various risk factors. Ignoring this heterogeneity can result in misleading conclusions. To deal with these problems, the proportional hazard frailty model was introduced. In this paper, the frailty model is explained as the product of the frailty random variable and baseline hazard rate. We examine the fit of the frailty model to the right-censored data from in the presence of explanatory variables (observable variables) and use it as a practical example to fit the frailty model to the data by considering the Weibull basis distribution and exponential in the likelihood functions. It is used to estimate the model parameters and compare the fit of the models with different criteria.
Mahsa Markani, Manije Sanei Tabas, Habib Naderi, Hamed Ahmadzadeh, Javad Jamalzadeh,
Volume 26, Issue 2 (3-2022)
Abstract
When working on a set of regression data, the situation arises that this data
It limits us, in other words, the data does not meet a set of requirements. The generalized entropy method is able to estimate the model parameters Regression is without applying any conditions on the error probability distribution. This method even in cases where the problem Too poorly designed (for example when sample size is too small, or data that has alignment
They are high and ...) is also capable. Therefore, the purpose of this study is to estimate the parameters of the logistic regression model using the generalized entropy of the maximum. A random sample of bank customers was collected and in this study, statistical work and were performed to estimate the model parameters from the binary logistic regression model using two methods maximum generalized entropy (GME) and maximum likelihood (ML). Finally, two methods were performed. We compare the mentioned. Based on the accuracy of MSE criteria to predict customer demand for long-term account opening obtained from logistic regression using both GME and ML methods, the GME method was finally more accurate than the ml method.
Somayeh Hutizadeh, Habib Naderi, ,
Volume 28, Issue 2 (3-2024)
Abstract
Drought is one of the most important concepts in hydrology, which has gained increased significance in recent years,
and the results of its modeling and analysis are crucial for risk assessment and management. This study examines drought at
the Zahedan station during the statistical period from 1951 to 2017 using the standardized precipitation index and explains
multivariate data modeling methods using Vine Copulas. Various models are compared using goodness-of-fit criteria, and
the best model is selected. Additionally, joint return periods are calculated and analyzed.
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