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Showing 4 results for Count Data
Hossein Baghishani, Mohammad Mahdi Tabatabaei,
Volume 1, Issue 1 (9-2007)
Abstract
In parameter driven models, the main problem is likelihood approximation and also parameter estimation. One approach to this problem is to apply simpler likelihoods such as composite likelihood. In this paper, we first introduce the parameter driven models and composite likelihood and then define a new model selection criterion based on composite likelihood. Finally, we demonstrate composite likelihood's capabilities in inferences and accurate model selection in parameter driven models throughout a simulation study.
Reza Pourmousa, Narjes Gilani,
Volume 11, Issue 2 (3-2018)
Abstract
In this paper the mixed Poisson regression model is discussed and a Poisson Birnbaum-Saunders regression model is introduced consider the over-dispersion. The Birnbaum-Saunders distribution is the mixture of two the generalized inverse Gaussian distributions, therefore it can be considered as an extension of traditional models. Our proposed model has less dimensional parameter space than the Poisson- generalized inverse Gaussian regression model. We also show that the proposed model has a closed form for likelihood function and we obtain its moments. The EM algorithm is used to estimate the parameters and its efficiency is compared with conventional models by a simulation study. An analysis of a real data is provided for more illustration.
Mahsa Nadifar, Hossein Baghishani, Afshin Fallah,
Volume 15, Issue 1 (9-2021)
Abstract
Many of spatial-temporal data, particularly in medicine and disease mapping, are counts. Typically, these types of count data have extra variability that distrusts the classical Poisson model's performance. Therefore, incorporating this variability into the modeling process, plays an essential role in improving the efficiency of spatial-temporal data analysis. For this purpose, in this paper, a new Bayesian spatial-temporal model, called gamma count, with enough flexibility in modeling dispersion is introduced. For implementing statistical inference in the proposed model, the integrated nested Laplace approximation method is applied. A simulation study was performed to evaluate the performance of the proposed model compared to the traditional models. In addition, the application of the model has been demonstrated in analyzing leukemia data in Khorasan Razavi province, Iran.
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.
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