Relevant bibliographies by topics / Zero-Inflated counts

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Academic literature on the topic 'Zero-Inflated counts'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Zero-Inflated counts"

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Preisser, John S., D. Leann Long, and John W. Stamm. "Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts." Caries Research 51, no. 3 (2017): 198–208. http://dx.doi.org/10.1159/000452675.

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Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 group
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O'Rourke, Holly, and Da Eun Han. "Considering the Distributional Form of Zeroes When Calculating Mediation Effects with Zero-Inflated Count Outcomes." Journal of Behavioral Data Science 3, no. 2 (2023): 1–14. http://dx.doi.org/10.35566/jbds/v3n2/orourke.

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Recent work has demonstrated how to calculate conditional mediated effects for mediation models with zero-inflated count outcomes in a non-causal framework (O’Rourke & Vazquez, 2019); however, those formulas do not distinguish between logistic and count portions of the data distribution when calculating mediated effects separately for zeroes and counts. When calculating conditional mediated effects for the counts in a zero-inflated count outcome Y, the b path should use the partial derivative of the log-linear regression equation for X and M predicting Y. When calculating conditional media
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Lee, Jong-Seung, and Hyung-Tae Ha. "Maximum-Likelihood Estimation for the Zero-Inflated Polynomial-Adjusted Poisson Distribution." Mathematics 13, no. 15 (2025): 2383. https://doi.org/10.3390/math13152383.

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We propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. 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. We derive the maximum-likelihood framework—including the log-likelihood and score equations under both general and regression settings—and fit zPAP to the zero-inflated, highly dispersed Fish Catch data as well as a synthetic bimodal mixture. In both cases, zPAP not only outperforms the standard zero-inflated Poisson mode
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A., Adetunji A., and Sabri S. R. M. "Generic Count Distributions and Their Zero-Inflated Forms: A Simulation Study." Mikailalsys Journal of Mathematics and Statistics 3, no. 2 (2025): 189–99. https://doi.org/10.58578/mjms.v3i2.5171.

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The percentage of zero observations necessitating zero-inflated distributions in count data modelling has been a major issue. The challenge in such a situation is determining when to shift from parent distributions to their zero-inflated versions. In most studies, the performances of parent distributions are assessed with those of their zero-inflated forms. This study conducts simulation studies for the Poisson and the negative binomial distributions and their respective zero-inflated forms. Count data [0, 4] with different percentages of zero counts are simulated using different sample sizes.
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Han, Bo, and Jian Xu. "Analysis of Crash Counts Using a Multilevel Zero-Inflated Negative Binomial Model." Advanced Materials Research 912-914 (April 2014): 1164–68. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1164.

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Due to that roadway crashes are generally discrete and rare, researchers frequently have several observational units (e.g., census tract, segment) with excess zeros reported crashes during the period. In this study, a multilevel zero-inflated negative binomial (MZINB) model was developed for analysis, allowing for overdispersion and excess zeros, as well as the factors of roadway design and traffic characteristic. Several goodness-of-fit measures are used for examining and comparing, using Markov chain Monte Carlo (MCMC) methods. The estimation results show that MZINB model is better than mult
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Alam, Morshed, Naim Al Mahi, and Munni Begum. "Zero-Inflated Models for RNA-Seq Count Data." Journal of Biomedical Analytics 1, no. 2 (2018): 55–70. http://dx.doi.org/10.30577/jba.2018.v1n2.23.

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One of the main objectives of many biological studies is to explore differential gene expression profiles between samples. Genes are referred to as differentially expressed (DE) if the read counts change across treatments or conditions systematically. Poisson and negative binomial (NB) regressions are widely used methods for non-over-dispersed (NOD) and over-dispersed (OD) count data respectively. However, in the presence of excessive number of zeros, these methods need adjustments. In this paper, we consider a zero-inflated Poisson mixed effects model (ZIPMM) and zero-inflated negative binomi
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MÖller, Tobias A., Christian H. Weiß, and Hee-Young Kim. "Modelling counts with state-dependent zero inflation." Statistical Modelling 20, no. 2 (2018): 127–47. http://dx.doi.org/10.1177/1471082x18800514.

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We introduce a state-dependent zero-inflation mechanism for count distributions with unbounded or bounded support. Instead of uniformly downweighting the parent distribution, this flexible approach allows us to generate most of the zeros from either low or high counts. We derive the stochastic properties of the inflated distributions and discuss special instances designed for zero inflation caused by, for example, excessive demand or underreporting. Furthermore, we apply the state-dependent zero-inflation mechanism to generalize existing models for count time series with bounded support.
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Dwivedi, Alok K., Sherif E. Elhanafi, Mohamed O. Othman, and Marc J. Zuckerman. "Zero-inflated models for the evaluation of colorectal polyps in colon cancer screening studies—a value-based biostatistics practice." PeerJ 13 (May 26, 2025): e19504. https://doi.org/10.7717/peerj.19504.

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Background Colon cancer screening studies are needed for the early detection of colorectal polyps to reduce the risk of colorectal cancer. Unfortunately, the data generated on colon polyps are typically analyzed in their dichotomized form and sometimes with standard count models, which leads to potentially inaccurate findings in research studies. A more appropriate approach for evaluating colon polyps is zero-inflated models, considering undetected existing polyps at colonoscopy screening. Method We demonstrated the application of the zero-inflated and hurdle models including zero-inflated Poi
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Sileshi, G. "Selecting the right statistical model for analysis of insect count data by using information theoretic measures." Bulletin of Entomological Research 96, no. 5 (2006): 479–88. http://dx.doi.org/10.1079/ber2006449.

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AbstractResearchers and regulatory agencies often make statistical inferences from insect count data using modelling approaches that assume homogeneous variance. Such models do not allow for formal appraisal of variability which in its different forms is the subject of interest in ecology. Therefore, the objectives of this paper were to (i) compare models suitable for handling variance heterogeneity and (ii) select optimal models to ensure valid statistical inferences from insect count data. The log-normal, standard Poisson, Poisson corrected for overdispersion, zero-inflated Poisson, the nega
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Cantoni, Eva, and Marie Auda. "Stochastic variable selection strategies for zero-inflated models." Statistical Modelling 18, no. 1 (2017): 3–23. http://dx.doi.org/10.1177/1471082x17711068.

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When count data exhibit excess zero, that is more zero counts than a simpler parametric distribution can model, the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) models are often used. Variable selection for these models is even more challenging than for other regression situations because the availability of p covariates implies 4 p possible models. We adapt to zero-inflated models an approach for variable selection that avoids the screening of all possible models. This approach is based on a stochastic search through the space of all possible models, which generates a
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Dissertations / Theses on the topic "Zero-Inflated counts"

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Ibukun, Michael Abimbola. "Modely s Touchardovým rozdělením." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445468.

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In 2018, Raul Matsushita, Donald Pianto, Bernardo B. De Andrade, Andre Cançado & Sergio Da Silva published a paper titled ”Touchard distribution”, which presented a model that is a two-parameter extension of the Poisson distribution. This model has its normalizing constant related to the Touchard polynomials, hence the name of this model. This diploma thesis is concerned with the properties of the Touchard distribution for which delta is known. Two asymptotic tests based on two different statistics were carried out for comparison in a Touchard model with two independent samples, supported by s
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Soares, Maria João de Sousa. "An avian relative fatality risk index for Iberian species on wind farms based on zero inflated count models." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13866.

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Mestrado em Biologia Aplicada<br>Climate change is one of the greatest threats towards humankind and wildlife. This consciousness motivated the search for alternatives that could contribute to mitigate climate change. Betting on renewable energies seems to be a winning strategy adopted worldwide in order to reduce greenhouse gas emissions responsible for global climate alterations and to improve nations’ energy independency. However, nowadays, these energy usages still have negative impacts, mostly on wildlife. Wind energy is even considered the greatest unintended human impact on avifauna. In
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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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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 mi
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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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Thomas, Gustavo. "GAMLSSs with applications to zero inflated and hierarquical data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06042018-150012/.

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The generalized additive models for location, scale and shape (GAMLSS) developed by Rigby and Stasinopoulos (2005) are a general class of univariate regression models that do not have the response distribution restricted to the exponential family as do the generalized linear and additive models, for example. In addition, they allow all the parameters of the response variable distribution to be modeled explicitly through different sets of explanatory variables. The semiparametric subclass of GAMLSS, in particular, accepts a wide range of parametric and nonparametric terms to be included in the
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Bhaktha, Nivedita. "Properties of Hurdle Negative Binomial Models for Zero-Inflated and Overdispersed Count data." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543573678017356.

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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
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Nian, Gaowei. "A score test of homogeneity in generalized additive models for zero-inflated count data." Kansas State University, 2014. http://hdl.handle.net/2097/18230.

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Master of Science<br>Department of Statistics<br>Wei-Wen Hsu<br>Zero-Inflated Poisson (ZIP) models are often used to analyze the count data with excess zeros. In the ZIP model, the Poisson mean and the mixing weight are often assumed to depend on covariates through regression technique. In other words, the effect of covariates on Poisson mean or the mixing weight is specified using a proper link function coupled with a linear predictor which is simply a linear combination of unknown regression coefficients and covariates. However, in practice, this predictor may not be linear in regression par
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Books on the topic "Zero-Inflated counts"

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Maiti, Raju. Modelling and coherent forecasting of zero-inflated time series count data. Indian Institute of Management, 2013.

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Philip, Loni P. Multilevel models for zero-inflated count data in environmental health and health disparities research. 2010.

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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 an
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Book chapters on the topic "Zero-Inflated counts"

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Borisov, Alexander, George Runger, Eugene Tuv, and Nuttha Lurponglukana-Strand. "Zero-Inflated Boosted Ensembles for Rare Event Counts." In Advances in Intelligent Data Analysis VIII. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03915-7_20.

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Wüthrich, Mario V., and Michael Merz. "Generalized Linear Models." In Springer Actuarial. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_5.

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AbstractThis chapter discusses state-of-the-art statistical modeling in insurance and actuarial science, which is the generalized linear model (GLM). We discuss GLMs in the light of claim count and claim size modeling, we present feature engineering, model fitting, model selection, over-dispersion, zero-inflated claim counts problems, double GLMs, and insurance-specific issues such as the balance property for having unbiasedness.
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Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, and Graham M. Smith. "Zero-Truncated and Zero-Inflated Models for Count Data." In Statistics for Biology and Health. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87458-6_11.

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Araldi, Alessandro, Alessandro Venerandi, and Giovanni Fusco. "Count Regression and Machine Learning Approach for Zero-Inflated Over-Dispersed Count Data. Application to Micro-Retail Distribution and Urban Form." In Computational Science and Its Applications – ICCSA 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_40.

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Inchausti, Pablo. "When the Response Variable is a Count, Often with Many Zeros." In Statistical Modeling With R. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192859013.003.0010.

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Abstract This chapter starts by discussing the Poisson and negative binomial distributions to model counts, and the associated log link function. It discusses over-dispersion, a common problem arising in the modeling of counts, along with its consequences and solutions. It illustrates the use of the negative binomial distribution as a continuous Poisson–gamma mixture to deal with over-dispersion, and finally covers the use of mixture models to model counts with an excess of zeros in the two main cases: zero-inflated and zero-augmented models. All the statistical models are fitted in both the f
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Stewart Sparks, Ross, and Hossein Hazrati-Marangaloo. "Exponentially Weighted Moving Averages of Counting Processes When the Time between Events Is Weibull Distributed." In Quality Control in Intelligent Manufacturing [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.90873.

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There are control charts for Poisson counts, zero-inflated Poisson counts, and over dispersed Poisson counts (negative binomial counts) but nothing on counting processes when the time between events (TBEs) is Weibull distributed. In our experience the in-control distribution for time between events is often Weibull distributed in applications. Counting processes are not Poisson distributed or negative binomial distributed when the time between events is Weibull distributed. This is a gap in the literature meaning that there is no help for practitioners when this is the case. This book chapter
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Tsang, Jung Yin, Matthew Sperrin, Thomas Blakeman, Rupert Payne, and Darren Ashcroft. "Risk Stratification Using a Novel Polypharmacy Assessment Score." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250586.

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Polypharmacy is a global health priority, characterized by an escalating prevalence of people using multiple medicines. Identifying people with problematic polypharmacy is complex, as multiple medicines can be suitable for people with several chronic conditions requiring more treatment. We present the development and internal validation of a novel approach to identifying potentially problematic polypharmacy. Medication count was modelled using zero-inflated negative binomial regression with age, gender, 37 long-term conditions and their grouped interactions as dependent variables. The “Polypha
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Fávero, Luiz Paulo, Patrícia Belfiore, and Rafael de Freitas Souza. "Count-data and zero-inflated regression models." In Data Science, Analytics and Machine Learning with R. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-12-824271-1.00012-3.

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Mashood, Lawal Olumuyiwa, Oluwafemi Samson Balogun, and Toluwalase Janet Akingbade. "An Application of Count Models to the Number of Antenatal Care Service Visits." In Advances in Healthcare Information Systems and Administration. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8103-5.ch016.

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This study examines the challenges of analyzing the effects of pregnant mothers visiting hospitals on their newborns during prenatal care in Katsina. Four Katsina public and private hospitals fitted expecting moms with four count models. The Vuong test and likelihood ratio test were used to assess if an over-dispersion parameter was needed. Zero-inflated negative binomial outperformed. The study found that a pregnant mother with a minimum of secondary education and a maximum of a first degree or higher national diploma (HND) while controlling for no formal education, using a private hospital/c
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Zhang, Wenbo, Xinwu Qian, and Satish V. Ukkusuri. "Identifying the Temporal Characteristics of Intra-City Movement Using Taxi Geo-Location Data." In Enriching Urban Spaces with Ambient Computing, the Internet of Things, and Smart City Design. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0827-4.ch004.

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In this chapter, the authors focus on temporal patterns of urban taxi trips and explore determinant factors in conjunction with geodatabase at aggregate level. Zero-Inflated Negative Binomial model is proposed in light of count data nature and excessive number of O-D pairs with zero trip. Three typical time slots on weekdays, as well as weekends, are introduced as case study to check temporal variations of intra-city movement. The results indicate that trip distance, land use, socioeconomics, and built environment are significant variables that affect the number of taxi trips between two locat
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Conference papers on the topic "Zero-Inflated counts"

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Ugalde-Araya, Isaí, and Erika Barrantes-Murillo. "Impact of Sample Size and Class Imbalance on Performance Metrics of Zero-Inflated Count Models: a Simulation Study Applied to Chagas Disease Data." In 2024 IEEE 6th International Conference on BioInspired Processing (BIP). IEEE, 2024. https://doi.org/10.1109/bip63158.2024.10885384.

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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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Fadhel, Atheer Ismael, and Hossein Jabbari Khamnei. "Bayesian Analysis of Zero-Inflated Count Data Using Beta-Lindley Distribution." In 2024 V International Conference on Neural Networks and Neurotechnologies (NeuroNT). IEEE, 2024. http://dx.doi.org/10.1109/neuront62606.2024.10585563.

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Li, Haoran. "Multilevel Modeling in Single-Case Studies With Zero-Inflated Count Data (Poster 10)." In AERA 2022. AERA, 2022. http://dx.doi.org/10.3102/ip.22.1881347.

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Li, Haoran. "Multilevel Modeling in Single-Case Studies With Zero-Inflated Count Data (Poster 10)." In 2022 AERA Annual Meeting. AERA, 2022. http://dx.doi.org/10.3102/1881347.

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Lu, Liying, Yingzi Fu, Peixiao Chu, and Xiaolin Zhang. "A Bayesian Analysis of Zero-Inflated Count Data: An Application to Youth Fitness Survey." In 2014 Tenth International Conference on Computational Intelligence and Security (CIS). IEEE, 2014. http://dx.doi.org/10.1109/cis.2014.125.

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SNEDDON, G., M. T. HASAN, and R. MA. "A UNIFIED APPROACH BETWEEN POPULATION-AVERAGED AND CLUSTER-SPECIFIC ANALYSES FOR MULTILEVEL ZERO-INFLATED COUNT DATA." In Proceedings of Statistics 2011 Canada/IMST 2011-FIM XX. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814417983_0016.

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