OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION by Yesan Tiara, Muhammad Nur Aidi, Erfiani Erfiani, Rika Rachmawati

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NEGATIVE BINOMIAL REGRESSION AND GENERALIZED POISSON REGRESSION MODELS ON THE NUMBER OF TRAFFIC ACCIDENTS IN CENTRAL JAVA by M Al Haris, Prizka Rismawati Arum

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MODELING THE MANY EARTHQUAKES IN SUMATRA USING POISSON HIDDEN MARKOV MODELS AND EXPECTATION MAXIMIZATION ALGORITHM by Muhammad Arib Alwansyah, Ramya Rachmawati

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Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution by Jong-Seung Lee, Hyung-Tae Ha

“…It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. …”
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POSITIVE CONFIRMED PREDICTION OF COVID-19 IN EAST JAVA USING COUNT TIME SERIES BASED DOUBLE POISSON INAR(p) PROCESS by A'yunin Sofro, Framitha Septian Subiantoro, Khusnia Nurul Khikmah

“…Because the count time series data shows overdispersion, predictions are made by modeling the COVID-19 data using the INAR( ). development model, namely Double Poisson INAR( ). …”
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Access Is Progress: Understanding Rural Secondary Student Access and Outcomes of Advanced Placement Courses by Phillip D. Grant, Ali Jahanaray, T. Logan Arrington

“…A zero-inflated negative binomial regression model (ZINB) was employed to assess AP course participation and AP exam performance while addressing overdispersion and excess zeros in the data. Key predictors included school locale (rural versus nonrural), state (Georgia versus South Carolina), and minoritized-majority status. …”
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On the Conflation of Poisson and Logarithmic Distributions with Applications by Abdulhamid A. Alzaid, Anfal A. Alqefari, Najla Qarmalah

“…These modifications yield distributions that demonstrate overdispersion models comparable to the negative binomial distribution (NBD) while retaining essential PD properties, making them suitable for accurately representing count data with frequent events of low frequency and high variance. …”
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Gut microbiota at different stages of cardiovascular disease continuum by A. A. Yafarova, E. D. Dementyeva, O. A. Zlobovskaya, A. R. Kiselev, A. F. Sheptulina, G. A. Ashniev, L. M. Zhamalov, V. V. Makarov, S. M. Yudin, O. M. Drapkina

“…Generalized linear models (QuasiPoisson, overdispersion) with covariates (age, body mass index) were used to compare groups by phylotype.Results. …”
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Bird population trend analyses for a monitoring scheme with a highly structured sampling design by Rieger, Mirjam R, Grüneberg, Christoph, Oberhaus, Michael, Trautmann, Sven, Parepa, Madalin, Anthes, Nils

“…However, monitoring data pose several challenges for statistical analysis, including spatial bias due to an unbalanced sampling of natural regions or habitats, variation in observer expertise, frequent observer changes, and overdispersion or zero-inflation in the raw data. An additional challenge arises from so-called ‘rolling’ survey designs, where each site is only visited once within each multi-year rotation cycle. …”
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The Wald-type Confidence Interval on the Mean Response Function of the Poisson Inverse Gaussian Ridge Regression by Muhammad Qasim, Y. Murat Bulut, Kristofer Månsson

“… The negative binomial (NB) regression model is commonly used to model overdispersed count data. However, the NB regression model is not suitable for highly overdispersed data, for which the Poisson-inverse Gaussian (PIG) regression model is often used instead. …”
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