Relevant bibliographies by topics / Zero inflated poisson regression

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Academic literature on the topic 'Zero inflated poisson regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "Zero inflated poisson regression"

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Jeong, Kwang Mo. "A Study on Tests for Zero-Inflated Poisson Regression." Korean Data Analysis Society 21, no. 6 (2019): 2737–49. http://dx.doi.org/10.37727/jkdas.2019.21.6.2737.

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Tang, Yi, and Wan Tang. "Testing modified zeros for Poisson regression models." Statistical Methods in Medical Research 28, no. 10-11 (2018): 3123–41. http://dx.doi.org/10.1177/0962280218796253.

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Excessive zeros are common in practice and may cause overdispersion and invalidate inferences when fitting Poisson regression models. Zero-inflated Poisson regression models may be applied if there are inflated zeros; however, it is desirable to test if there are inflated zeros before such zero-inflated Poisson models are applied. Assuming a constant probability of being a structural zero in a zero-inflated Poisson regression model, the existence of the inflated zeros may be tested by testing whether the constant probability is zero. In such situations, the Wald, score, and likelihood ratio tests can be applied. Without specifying a zero-inflated Poisson model, He et al. recently developed a test by comparing the amount of observed zeros with that expected under the Poisson model. In this paper, we develop a closed form for the test and compare it with the Wald, score, and likelihood ratio tests through simulation studies. The simulation studies show that the test of He et al. is the best in controlling type I errors, while the score test generally has the least power among the tests. The tests are illustrated with two real data examples.
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Lim, Hwa Kyung, Wai Keung Li, and Philip L. H. Yu. "Zero-inflated Poisson regression mixture model." Computational Statistics & Data Analysis 71 (March 2014): 151–58. http://dx.doi.org/10.1016/j.csda.2013.06.021.

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Famoye, Felix, and John S. Preisser. "Marginalized zero-inflated generalized Poisson regression." Journal of Applied Statistics 45, no. 7 (2017): 1247–59. http://dx.doi.org/10.1080/02664763.2017.1364717.

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Lee, Dong-Hee, and Byoung Cheol Jung. "A Study on the Effect of Dispersion Parameter in a Zero-inflated Generalized Poisson Regression Model." Korean Data Analysis Society 27, no. 1 (2025): 105–15. https://doi.org/10.37727/jkdas.2025.27.1.105.

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In this study, we examine the influence of overdispersion on statistical inference in a zero-inflated generalized Poisson regression model by simulation experiments and real data analysis. In the simulation study, simulated data are generated from the zero-inflated generalized Poisson regression model, and the regression coefficients in the zero-inflated Poisson regression and the zero-inflated generalized Poisson regression models are estimated. The simulation experiment results show that the regression coefficient estimates for the mean and zero-inflation probability of the zero-inflated Poisson regression to ignore overdispersion underestimate the true value when overdispersion exists, and this underestimation problem becomes more severe as the overdispersion increases. In addition, the standard errors of the regression coefficient estimates of the zero-inflated Poisson regression model are more underestimated as the overdispersion increased. Ultimately, the zero-inflated Poisson regression model, which ignores the overdispersion despite its existence, shows a larger nominal significance level in the hypothesis test for the true value of the regression coefficient, and this problem of the nominal significance level becomes more severe as the overdispersion increased. The results of this simulation experiment could be confirmed through the analysis of real data on the number of boat trips to Lake Somerville in eastern Texas in the United States in 1980.
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Zamani, Hossein, and Noriszura Ismail. "Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives." Journal of Applied Statistics 40, no. 9 (2013): 2056–68. http://dx.doi.org/10.1080/02664763.2013.804904.

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He, Hua, Hui Zhang, Peng Ye, and Wan Tang. "A test of inflated zeros for Poisson regression models." Statistical Methods in Medical Research 28, no. 4 (2017): 1157–69. http://dx.doi.org/10.1177/0962280217749991.

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Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.
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Azad, Abdulhafedh. "Incorporating Zero-Inflated Poisson (ZIP) Regression Model in Crash Frequency Analysis." International Journal of Novel Research in Interdisciplinary Studies 10, no. 1 (2023): 6–18. https://doi.org/10.5281/zenodo.7632596.

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<strong>Abstract:</strong> This paper addresses the Zero-inflated Poisson (ZIP) regression model as an effective way to handle the excess zeros that usually exist in vehicle crash data and to allow for possible overdispersion in the data. The ZIP model is based on a zero-inflated probability distribution, that allows for frequent zero-valued observations. When the number of zeros is large that the data do not fit standard distributions (e.g., normal, Poisson, binomial, negative-binomial, and beta), the data is referred to as zero inflated. A dual state crash system is assumed in the ZIP model, in which one state is the zero crash state that can be regarded as virtually safe during the observation period, while the other state is the non-zero crash state. This paper starts by applying a multiple linear regression model, a Poisson regression model, a Negative Binomial regression model and then introduces the ZIP model to analyze the 2013-2015 crash data for the Interstate I-94 in the State of Minnesota in the US. Results show that the ZIP model overperformed the other models by fitting the crash data much better and was able to capture almost all the independent variables in the model and make them statistically significant in the analysis after being insignificant by the other models. <strong>Keywords:</strong> Zero-Inflated Poisson Regression, ZIP model, Crash Frequency, Multiple Linear Regression, Poisson Regression, Negative Binomial Regression. <strong>Title:</strong> Incorporating Zero-Inflated Poisson (ZIP) Regression Model in Crash Frequency Analysis <strong>Author:</strong> Azad Abdulhafedh <strong>International Journal of Novel Research in Interdisciplinary Studies</strong> <strong>ISSN 2394-9716</strong> <strong>Vol. 10, Issue 1, January 2023 - February 2023</strong> <strong>Page No: 6-18</strong> <strong>Novelty Journals</strong> <strong>Website: www.noveltyjournals.com</strong> <strong>Published Date: 11-February-2022</strong> <strong>DOI: https://doi.org/10.5281/zenodo.7632596</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.noveltyjournals.com/upload/paper/Incorporating%20Zero-Inflated-11022023-2.pdf</strong>
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Weni Utomo, Candra Rezzing, Achmad Efendi, and Ni Wayan S. Wardhani. "Simulation Study of Bayesian Zero Inflated Poisson Regression." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 213–23. https://doi.org/10.18860/cauchy.v10i1.30207.

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Bayesian merupakan salah satu metode estimasi parameter yang dapat diaplikasikan pada ukuran sampel yang kecil. Zero Inflated Poisson merupakan salah satu metode untuk menganalisis data Poisson yang mengalami overdispersion. Tujuan dari penelitian ini adalah untuk mengevaluasi kinerja analisis Zero Inflated Poisson Regression menggunakan Bayesian. Data yang digunakan adalah jumlah kasus campak di Jawa Timur. Campak merupakan penyakit menular yang berpotensi menjadi wabah di berbagai daerah, termasuk Jawa Timur. Terdapat empat variabel prediktor yang digunakan yaitu Jumlah Penduduk (X1), Persentase Vaksinasi (X2), Persentase Penduduk Miskin (X3), dan Persentase Sanitasi Layak (X4), serta satu variabel respon yaitu Jumlah Kasus Campak. Hasil penelitian ini menunjukkan bahwa estimasi model Zero Inflated Poisson (ZIP) menggunakan Bayesian lebih baik dibandingkan estimasi model Zero Inflated Poisson (ZIP) menggunakan MLE. Hal ini dikarenakan data yang digunakan dalam penelitian memiliki sampel yang kecil sehingga estimasi MLE cenderung kurang baik digunakan dalam estimasi parameter. Pemilihan model terbaik dilakukan dengan menggunakan metode Deviance Information Criteria (DIC). Model terbaik ditunjukkan dengan nilai DIC terkecil pada ukuran sampel 100 dan proporsi nol 0,8.
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Chakraborty, Sukanta, and Soma Chowdhury Biswas. "MODELLING ZERO-INFLATED OVER DISPERSED DENGUE DATA VIA ZERO-INFLATED POISSON INVERSE GAUSSIAN REGRESSION MODEL: A CASE STUDY OF BANGLADESH." Acta Scientifica Malaysia 8, no. 1 (2024): 11–14. https://doi.org/10.26480/asm.01.2024.11.14.

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Bangladesh has been noted for experiencing some of the most susceptible dengue outbreaks in Asia; the country’s location, dense population, and changing environment all play major roles in this. Determining the correlation between meteorological conditions and case count is critical for predicting about the characteristics of the DENV outbreak. Certain widely used models, such as the Poisson regression model or the negative binomial regression model, are insufficient to adequately predict dengue fever since many of these datasets are of the over-dispersed, long-tail variety, and zero-inflated. In this study, the Zero-inflated negative binomial regression model is compared with the Zero-inflated Poisson inverse Gaussian regression model. Depending on AIC and BIC criteria Zero-inflated Poisson inverse-Gaussian regression model is proposed. Then Zero-inflated Poisson inverse Gaussian regression model is used to model the dataset containing confirmed positive cases of dengue fever and seven meteorological variables. The proposed model shows that all the meteorological variables are significantly associated with the confirmed positive cases of dengue fever. That’s why modeling a dengue-fever dataset with a Zero-inflated Poisson-inverse Gaussian regression model is suggested in this study.
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Dissertations / Theses on the topic "Zero inflated poisson regression"

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Roemmele, Eric S. "A Flexible Zero-Inflated Poisson Regression Model." UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/38.

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A practical problem often encountered with observed count data is the presence of excess zeros. Zero-inflation in count data can easily be handled by zero-inflated models, which is a two-component mixture of a point mass at zero and a discrete distribution for the count data. In the presence of predictors, zero-inflated Poisson (ZIP) regression models are, perhaps, the most commonly used. However, the fully parametric ZIP regression model could sometimes be restrictive, especially with respect to the mixing proportions. Taking inspiration from some of the recent literature on semiparametric mixtures of regressions models for flexible mixture modeling, we propose a semiparametric ZIP regression model. We present an "EM-like" algorithm for estimation and a summary of asymptotic properties of the estimators. The proposed semiparametric models are then applied to a data set involving clandestine methamphetamine laboratories and Alzheimer's disease.
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Wan, Chung-him, and 溫仲謙. "Analysis of zero-inflated count data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43703719.

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Wan, Chung-him. "Analysis of zero-inflated count data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43703719.

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Jansakul, Naratip. "Some aspects of modelling overdispersed and zero-inflated count data." Thesis, University of Exeter, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364435.

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Prasad, Jonathan P. "Zero-Inflated Censored Regression Models: An Application with Episode of Care Data." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2226.

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The objective of this project is to fit a sequence of increasingly complex zero-inflated censored regression models to a known data set. It is quite common to find censored count data in statistical analyses of health-related data. Modeling such data while ignoring the censoring, zero-inflation, and overdispersion often results in biased parameter estimates. This project develops various regression models that can be used to predict a count response variable that is affected by various predictor variables. The regression parameters are estimated with Bayesian analysis using a Markov chain Monte Carlo (MCMC) algorithm. The tests for model adequacy are discussed and the models are applied to an observed data set.
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Zeileis, Achim, Christian Kleiber, and Simon Jackman. "Regression Models for Count Data in R." Foundation for Open Access Statistics, 2008. http://epub.wu.ac.at/4986/1/Zeileis_etal_2008_JSS_Regression%2DModels%2Dfor%2DCount%2DData%2Din%2DR.pdf.

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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences-better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (authors' abstract)
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Zeileis, Achim, Christian Kleiber, and Simon Jackman. "Regression Models for Count Data in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/1168/1/document.pdf.

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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model classes are able to incorporate over-dispersion and excess zeros - two problems that typically occur in count data sets in economics and the social and political sciences - better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice. (author's abstract)<br>Series: Research Report Series / Department of Statistics and Mathematics
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Rogers, Jennifer Kathleen. "Safety Benchmarking of Industrial Construction Projects Based on Zero Accidents Techniques." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/42859.

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Safety is a continually significant issue in the construction industry. The Occupation Safety and Health Administration as well as individual construction companies are constantly working on verifying that their selected safety plans have a positive effect on reduction of workplace injuries. Worker safety is a large concern for both the workers and employers in construction and the government also attempts to impose effective regulations concerning minimum safety requirements. There are many different methods for creating and implementing a safety plan, most notably the Construction Industry Instituteâ s (CII) Zero Accidents Techniques (ZAT). This study will attempt to identify a relationship between the level of ZAT implementation and safety performance on industrial construction projects. This research also proposes that focusing efforts on certain ZAT elements over others will show different safety performance results. There are three findings in this study that can be used to assist safety professionals in designing efficient construction safety plans. The first is a significant log-log relationship that is identified between the DEA efficiency scores and Recordable Incident Rate (RIR). There is also a significant difference in safety performance found between the Light Industrial and Heavy Industrial sectors. Lastly, regression is used to show that the pre-construction and worker selection ZAT components can predict a better safety performance.<br>Master of Science
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Liu, Hai Chan Kung-sik. "Semiparametric regression analysis of zero-inflated data." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/308.

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Liu, Hai. "Semiparametric regression analysis of zero-inflated data." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/308.

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Zero-inflated data abound in ecological studies as well as in other scientific and quantitative fields. Nonparametric regression with zero-inflated response may be studied via the zero-inflated generalized additive model (ZIGAM). ZIGAM assumes that the conditional distribution of the response variable belongs to the zero-inflated 1-parameter exponential family which is a probabilistic mixture of the zero atom and the 1-parameter exponential family, where the zero atom accounts for an excess of zeroes in the data. We propose the constrained zero-inflated generalized additive model (COZIGAM) for analyzing zero-inflated data, with the further assumption that the probability of non-zero-inflation is some monotone function of the (non-zero-inflated) exponential family distribution mean. When the latter assumption obtains, the new approach provides a unified framework for modeling zero-inflated data, which is more parsimonious and efficient than the unconstrained ZIGAM. We develop an iterative algorithm for model estimation based on the penalized likelihood approach, and derive formulas for constructing confidence intervals of the maximum penalized likelihood estimator. Some asymptotic properties including the consistency of the regression function estimator and the limiting distribution of the parametric estimator are derived. We also propose a Bayesian model selection criterion for choosing between the unconstrained and the constrained ZIGAMs. We consider several useful extensions of the COZIGAM, including imposing additive-component-specific proportional and partial constraints, and incorporating threshold effects to account for regime shift phenomena. The new methods are illustrated with both simulated data and real applications. An R package COZIGAM has been developed for model fitting and model selection with zero-inflated data.
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Books on the topic "Zero inflated poisson regression"

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Ling, Wodan. Quantile regression for zero-inflated outcomes. [publisher not identified], 2019.

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author, Ieno Elena N., ed. Beginner's guide to zero-inflated models with R. Highland Statistics Ltd., 2016.

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Buu, Anne, and Runze Li. New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676001.003.0021.

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This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.
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Book chapters on the topic "Zero inflated poisson regression"

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Asar, Yasin, S. Ejaz Ahmed, and Bahadır Yüzbaşı. "Efficient and Improved Estimation Strategy in Zero-Inflated Poisson Regression Models." In Proceedings of the Twelfth International Conference on Management Science and Engineering Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93351-1_27.

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Husin, Muhammad′ Afif Amir, and Mohd Fadzli Mohd Fuzi. "Bayesian Statistical Modeling: Comparisons Between Poisson and Its Zero-Inflated Regression Model." In Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017). Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7279-7_32.

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Lukusa, M. T., and F. K. H. Phoa. "Semiparametric Weighting Estimations of a Zero-Inflated Poisson Regression with Missing in Covariates." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57306-5_30.

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Iannario, Maria, Ioannis Ntzoufras, and Claudia Tarantola. "Zero Inflated Bivariate Poisson Regression Models for a Sport (in)activity Data Analysis." In Models for Data Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15885-8_11.

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Sheth-Chandra, Manasi, N. Rao Chaganty, and Roy T. Sabo. "A Doubly-Inflated Poisson Distribution and Regression Model." In STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11431-2_7.

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Chow, Shein-Chung, Jun Shao, Hansheng Wang, and Yuliya Lokhnygina. "Test for Homogeneity of Two Zero-Inflated Poisson Population." In Sample Size Calculations in Clinical Research: Third Edition. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315183084-16.

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Mizutani, Keisuike, Ayaka Ueta, Ryota Ueda, et al. "Zero-Inflated Poisson Tensor Factorization for Sparse Purchase Data in E-Commerce Markets." In Industrial Engineering and Applications – Europe. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58113-7_14.

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Junnumtuam, Sunisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "The Bayesian Confidence Interval for the Mean of the Zero-Inflated Poisson Distribution." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62509-2_35.

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Bose, Nadashree, and Hemlata Joshi. "Optimizing Healthcare Analytics: A Zero-Inflated Poisson Approach to Pediatric Emergency Room Visits." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78946-5_13.

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Baione, Fabio, Davide Biancalana, and Paolo De Angelis. "An Application of Zero-One Inflated Beta Regression Models for Predicting Health Insurance Reimbursement." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_12.

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Conference papers on the topic "Zero inflated poisson regression"

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Shkera, Ali, and Vaishali Patankar. "Zero-Inflated Poisson regression: Application to pedestrian travel behavior." In RECENT TRENDS IN APPLIED MATHEMATICS IN SCIENCE AND ENGINEERING. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0137174.

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Nariswari, Rinda, Afifa Ayu Widhiyanthi, Samsul Arifin, and I. Gusti Agung Anom Yudistira. "Zero inflated Poisson Regression: A solution of overdispersion in stunting data." In 4TH INTERNATIONAL SCIENTIFIC CONFERENCE OF ALKAFEEL UNIVERSITY (ISCKU 2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0181105.

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Lichman, Moshe, and Padhraic Smyth. "Prediction of Sparse User-Item Consumption Rates with Zero-Inflated Poisson Regression." In the 2018 World Wide Web Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3178876.3186153.

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Sari, Dewi Novita, Purhadi, Santi Puteri Rahayu, and Irhamah. "Bivariate zero-inflated generalized Poisson regression on modelling stillbirth and maternal death." In 7TH INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Mathematics of Quantum Computing. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0115756.

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Kusuma, Rahmaniar Dwinta, and Yogo Purwono. "Zero-Inflated Poisson Regression Analysis On Frequency Of Health Insurance Claim PT. XYZ." In Proceedings of the 12th International Conference on Business and Management Research (ICBMR 2018). Atlantis Press, 2019. http://dx.doi.org/10.2991/icbmr-18.2019.52.

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Ermawati, Purhadi, and Santi Puteri Rahayu. "Parameter estimation and statistical test on zero inflated Poisson inverse Gaussian regression model." In THE 8TH INTERNATIONAL CONFERENCE AND WORKSHOP ON BASIC AND APPLIED SCIENCE (ICOWOBAS) 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0104166.

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Zhang, Chen, Nan Chen, and Linmiao Zhang. "Time series of multivariate zero-inflated Poisson counts." In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2016. http://dx.doi.org/10.1109/ieem.2016.7798101.

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Al-Sayed, A. M., T. Mahmood, and H. H. Saleh. "Residual Based Control Charts for Zero-inflated Poisson Processes." In 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2022. http://dx.doi.org/10.1109/ieem55944.2022.9989910.

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Hartatik, Nurani, Joewono Prasetijo, Yudi Dwi Prasetyo, Khilda Nistrina, and Atqiya Muslihati. "Zero-inflated regression models for measuring accident." In PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY, AND INDUSTRIAL APPLICATIONS 2021 (8th ICETIA 2021): Engineering, Environment, and Health: Exploring the Opportunities for the Future. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0180308.

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Pratama, Jaka, and Achmad Choiruddin. "Species Distribution Modeling with Spatial Point Process: Comparing Poisson and Zero Inflated Poisson-Based Algorithms." In 2022 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2022. http://dx.doi.org/10.1109/icodsa55874.2022.9862862.

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Reports on the topic "Zero inflated poisson regression"

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Moral, Rafael. Introduction to Generalized Linear Models. Instats Inc., 2024. http://dx.doi.org/10.61700/vteee3zjf6fsm1478.

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This seminar provides a comprehensive introduction to Generalized Linear Models (GLMs), covering binary, binomial, categorical logistic regression, Poisson regression, and advanced topics like overdispersion and zero-inflated models. Participants will gain theoretical knowledge and practical skills in applying GLMs using R, enhancing their ability to perform rigorous statistical analyses in various research scenarios.
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