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Showing 2 results for Zero-Inflated
Ehsan Bahrami Samani, Nafeseh Khojasteh Bakht,
Volume 14, Issue 1 (8-2020)
Abstract
In this paper, the analysis of count response with many zeros, named as zero-inflated data, is considered. Assumes that responses follow a zero-inflated power series distribution. Because of there is missing of the type of random in the covariate, some of the data application, various methods for estimating of parameters by using the score function with and without missing data for the proposed regression model are presented. On the other hand, known or unknown selection probability in the missing covariates results in presenting a semi-parametric method for estimating of parameters in the zero-inflated power series regression model. To illustrate the proposed method, simulation studies and a real example are applied. Finally, the performance of the semi-parametric method is compared with maximum likelihood, complete-case and inverse probability weighted method.
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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