|
|
|
 |
Search published articles |
 |
|
Showing 4 results for Torabi
Hamazeh Torabi, Narges Montazeri, Fatemeh Ghasemian,
Volume 7, Issue 2 (3-2014)
Abstract
In this paper, some various families constructed from the logit of the generalized Beta, Beta, Kumar, generalized Gamma, Gamma, Weibull, log gamma and Logistic distributions are reviewed. Then a general family of distributions generated from the logit of the normal distribution is proposed. A special case of this family, Normal-Uniform distribution, is defined and studied. Various properties of the distribution are also explored. The maximum likelihood and minimum spacings estimators of the parameters of this distribution are obtained. Finally, the new distribution is effectively used to analysis a real survival data set.
Mohamad Bayat, Hamzeh Torabi,
Volume 12, Issue 1 (9-2018)
Abstract
Nowadays, the use of various censorship methods has become widespread in industrial and clinical tests. Type I and Type II progressive censoring are two types of these censors. The use of these censors also has some disadvantages. This article tries to reduce the defects of the type I progressive censoring by making some change to progressive censorship. Considering the number and the time of the withdrawals as a random variable, this is done. First, Type I, Type II progressive censoring and two of their generalizations are introduced. Then, we introduce the new censoring based on the Type I progressive censoring and its probability density function. Also, some of its special cases will be explained and a few related theorems are brought. Finally, the simulation algorithm is brought and for comparison of introduced censorship against the traditional censorships a simulation study was done.
Hossein Nadeb, Hamzeh Torabi,
Volume 13, Issue 1 (9-2019)
Abstract
In this paper, a general method for goodness of fit test for the location-scale family of distributions under Type-II progressive censoring is presented and its properties are investigated. Then, using Monte Carlo simulation studies, the power of this test is compared with the powers of some existing tests for testing the Gumbel distribution. Finally the proposed test is used for fitting a distribution to a real data set.
Taranom Torabi Neman, Mahdi Emadi, Mohammad Arashi,
Volume 20, Issue 1 (9-2026)
Abstract
In the broad landscape of modern data analysis, dealing with count data affected by excessive zeros represents a fundamental analytical challenge. Classical models such as Poisson regression exhibit notable weaknesses in this context, as they cannot distinguish between structural zeros (arising from deterministic processes) and random zeros (arising from stochastic processes). Although zero-inflated Poisson regression models have taken a significant step toward addressing this issue, their performance is seriously limited in the era of high-dimensional and large-scale data.
This research, looking beyond traditional frameworks, introduces an innovative deep neural network–based framework designed to enhance and transform zero-inflated Poisson regression models. By leveraging the remarkable capacity of deep learning to extract nonlinear and complex features, this hybrid approach can model the dual data-generating processes, the zero-generation process, and the count-generation process with unprecedented precision.
|
|
