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Showing 4 results for Madadi
Bahareh Afhami, Mohsen Madadi, Mohsen Rezapour,
Volume 9, Issue 1 (9-2015)
Abstract
In this paper, first the Shannon entropy of k-record values is derived from the generalized Pareto distribution and propose goodness-of-fit tests based on this entropy. Finally, real data and a simulation study are used for analyzing the performance of this statistic.
Mousa Abdi, Mohsen Madadi, Ahad Jamalizadeh,
Volume 14, Issue 2 (2-2021)
Abstract
In this article, a mixture of multivariate normal and standard exponential distributions is investigated. It is shown that the range of skewness and kurtosis coefficients for this distribution is wider than that of the skew-normal distribution. Some properties of this distribution, such as characteristic function, moment generating function, four first moments, skewness and kurtosis of distribution are presented. Also, the distribution of offine transformations and canonical forms of distribution are derived. The maximum likelihood estimation of parameters of the model is computed by using an EM algorithm. To investigate the suitability and efficiency of the model, a simulation study is presented. Finally, two numerical examples with real data sets are studied.
Dr. Me'raj Abdi, Dr. Mohsen Madadi,
Volume 17, Issue 1 (9-2023)
Abstract
This paper proposes a different attitude for analyzing three-way contingency tables using conditional independence. We show that different types of independence explored in log-linear models can be achieved without using these models and only by using conditional independence. Some numerical examples are presented to illustrate the proposed methods.
Mehrnoosh Madadi, Kiomars Motarjem,
Volume 18, Issue 2 (2-2025)
Abstract
Due to the volume and complexity of emerging data in survival analysis, it is necessary to use statistical learning methods in this field. These methods can estimate the probability of survival and the effect of various factors on the survival of patients. In this article, the performance of the Cox model as a common model in survival analysis is compared with compensation-based methods such as Cox Ridge and Cox Lasso, as well as statistical learning methods such as random survival forests and neural networks. The simulation results show that in linear conditions, the performance of the models mentioned above is similar to the Cox model. In non-linear conditions, methods such as Cox lasso, random survival forest, and neural networks perform better. Then, these models were evaluated in the analysis of the data of patients with atheromatous, and the results showed that when faced with data with a large number of explanatory variables, statistical learning approaches generally perform better than the classical survival model.
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