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Journal articles on the topic 'Zero-Inflated counts'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

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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Nketia, Kojo, and Dziedzom K. de Souza. "Using zero-inflated and hurdle regression models to analyze schistosomiasis data of school children in the southern areas of Ghana." PLOS ONE 19, no. 7 (2024): e0304681. http://dx.doi.org/10.1371/journal.pone.0304681.

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Background Schistosomiasis is a neglected disease prevalent in tropical and sub-tropical areas of the world, especially in Africa. Detecting the presence of the disease is based on the detection of the parasites in the stool or urine of children and adults. In such studies, typically, data collected on schistosomiasis infection includes information on many negative individuals leading to a high zero inflation. Thus, in practice, counts data with excessive zeros are common. However, the purpose of this analysis is to apply statistical models to the count data and evaluate their performance and

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Jang, Jong-Hwan, Junggu Choi, Hyun Woong Roh, et al. "Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study." JMIR mHealth and uHealth 8, no. 7 (2020): e16113. http://dx.doi.org/10.2196/16113.

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Background Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks. Objective The aim of this study was to impute missing values in data using a deep learning approa

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Purhadi, Yuliani Setia Dewi, and Luthfatul Amaliana. "Zero Inflated Poisson and Geographically Weighted Zero- Inflated Poisson Regression Model: Application to Elephantiasis (Filariasis) Counts Data." Journal of Mathematics and Statistics 11, no. 2 (2015): 52–60. http://dx.doi.org/10.3844/jmssp.2015.52.60.

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DENWOOD, M. J., M. J. STEAR, L. MATTHEWS, S. W. J. REID, N. TOFT, and G. T. INNOCENT. "The distribution of the pathogenic nematodeNematodirus battusin lambs is zero-inflated." Parasitology 135, no. 10 (2008): 1225–35. http://dx.doi.org/10.1017/s0031182008004708.

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SUMMARYUnderstanding the frequency distribution of parasites and parasite stages among hosts is essential for efficient experimental design and statistical analysis, and is also required for the development of sustainable methods of controlling infection.Nematodirus battusis one of the most important organisms that infect sheep but the distribution of parasites among hosts is unknown. An initial analysis indicated a high frequency of animals withoutN. battusand with zero egg counts, suggesting the possibility of a zero-inflated distribution. We developed a Bayesian analysis using Markov chain

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Ghosh, Souparno, Alan E. Gelfand, Kai Zhu, and James S. Clark. "The k-ZIG: Flexible Modeling for Zero-Inflated Counts." Biometrics 68, no. 3 (2012): 878–85. http://dx.doi.org/10.1111/j.1541-0420.2011.01729.x.

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Pittman, Brian, Eugenia Buta, Suchitra Krishnan-Sarin, Stephanie S. O’Malley, Thomas Liss, and Ralitza Gueorguieva. "Models for Analyzing Zero-Inflated and Overdispersed Count Data: An Application to Cigarette and Marijuana Use." Nicotine & Tobacco Research 22, no. 8 (2018): 1390–98. http://dx.doi.org/10.1093/ntr/nty072.

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Abstract Introduction This article describes different methods for analyzing counts and illustrates their use on cigarette and marijuana smoking data. Methods The Poisson, zero-inflated Poisson (ZIP), hurdle Poisson (HUP), negative binomial (NB), zero-inflated negative binomial (ZINB), and hurdle negative binomial (HUNB) regression models are considered. The different approaches are evaluated in terms of the ability to take into account zero-inflation (extra zeroes) and overdispersion (variance larger than expected) in count outcomes, with emphasis placed on model fit, interpretation, and choo

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Brodziak, Jon, and William A. Walsh. "Model selection and multimodel inference for standardizing catch rates of bycatch species: a case study of oceanic whitetip shark in the Hawaii-based longline fishery." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 12 (2013): 1723–40. http://dx.doi.org/10.1139/cjfas-2013-0111.

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One key issue for standardizing catch per unit effort (CPUE) of bycatch species is how to model observations of zero catch per fishing operation. Typically, the fraction of zero catches is high, and catch counts may be overdispersed. In this study, we develop a model selection and multimodel inference approach to standardize CPUE in a case study of oceanic whitetip shark (Carcharhinus longimanus) bycatch in the Hawaii-based pelagic longline fishery. Alternative hypotheses for shark catch per longline set were characterized by the variance to mean ratio of the count distribution. Zero-inflated

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Zhu, Huirong, Stacia M. DeSantis, and Sheng Luo. "Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective." Statistical Methods in Medical Research 27, no. 4 (2016): 1258–70. http://dx.doi.org/10.1177/0962280216659312.

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Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-

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Habeeb Hashim, Luay, and Ahmad Naeem Flaih. "Modeling the Rainfall Count data Using Some Zero Type models with application." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (2019): 14–27. http://dx.doi.org/10.29304/jqcm.2019.11.2.554.

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Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behaviora

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S. VISHNU SHANKAR, R. AJAYKUMAR, P. PRABHAKARAN, R. KUMARAPERUMAL, and M. GUNA. "Modelling of tea mosquito bug (Helopeltis theivora) incidence on neem tree: A zero inflated count data analysis." Journal of Agrometeorology 24, no. 4 (2022): 409–16. http://dx.doi.org/10.54386/jam.v24i4.1891.

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Neem (Azadirachta indica) is an evergreen tree belonging to the Meliaceae family and is highly infected by the seasonal pest called Helopeltis theivora, the tea mosquito bug. The study monitors the pest infection between May 2019 and April 2021 by the direct counting method. Weekly counts of insect pest population were found to be correlated with weather parameters viz., maximum temperature (Tmax.), minimum temperature (Tmin), relative humidity [morning (07.22hrs) and afternoon (14.22hrs) (RH)], rainfall (mm/day) and wind speed (km/h). Zero inflated count data techniques were opted for modelli

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Vagelas, Ioannis. "Analysis of Over-Dispersed Count Data: Application to Obligate Parasite Pasteuria Penetrans." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 18 (March 1, 2022): 333–39. http://dx.doi.org/10.37394/232015.2022.18.33.

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In this article we present with STATA regression models suitable for analyzing over-dispersed count outcomes. Specifically, the Negative Binomial regression can be an appropriate choice for modeling count variables, usually for over-dispersed count outcome variables. The common problem with count data with zeroes is that the empirical data often show more zeroes than would be expected under either Poisson or the Negative Binomial model. We concluded, this publications showcases that Zero-inflated models can be used to model count data that has excessive zero counts.

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Afroz, Farzana, Md Muddasir Hossain Akib, Bikash Pal, and Abida Sultana Asha. "Impact of parental education on number of under five children death per mother in Bangladesh." PLOS ONE 20, no. 2 (2025): e0318787. https://doi.org/10.1371/journal.pone.0318787.

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One of the leading challenges of social development is the reduction of children’s deaths under the age of five. The primary focus of this research is to study the potential impact of parental education on under five children death in Bangladesh utilizing a secondary dataset extracted from the Bangladesh Demographic and Health Survey (BDHS), 2017–18. The total count of deceased children within a family is a non-negative numerical variable. The mean number of under five children death per 100 mothers is found to be 20 with variance of around 27, which indicates the presence of overdispersion. A

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ALPAY, Olcay, Nazan DANACIOĞLU, and Emel ÇANKAYA. "Modelling of Factors Influencing the Citation Counts in Statistics." Academic Platform Journal of Engineering and Smart Systems 10, no. 3 (2022): 157–67. http://dx.doi.org/10.21541/apjess.1075099.

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Citation is considered as the most popular quality assessment metric for scientific papers, and it is thus important to determine what factors promote the citation count of a paper in comparison to the others in the same field. The main aim of this study is to model the citation counts of the research published in SCI or SCI-Expanded journals of Statistics field with the growing number of scientific works in Turkey. It is well known that the right-skewed nature of the counts makes the classical regression modelling inappropriate, even if the log transformation of counts is applied [1]. Due to

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Mathews, Joseph, Sumangal Bhattacharya, Sumen Sen, and Ishapathik Das. "Multiple inflated negative binomial regression for correlated multivariate count data." Dependence Modeling 10, no. 1 (2022): 290–307. http://dx.doi.org/10.1515/demo-2022-0149.

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Abstract This article aims to provide a method of regression for multivariate multiple inflated count responses assuming the responses follow a negative binomial distribution. Negative binomial regression models are common in the literature for modeling univariate and multivariate count data. However, two problems commonly arise in modeling such data: choice of the multivariate form of the underlying distribution and modeling the zero-inflated structure of the data. Copula functions have become a popular solution to the former problem because they can model the response variables’ dependence s

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Beveridge, Max, Zach Goldstein, and Hee Cheol Chung. "A Comparison of Zero-Inflated Models for Modern Biomedical Data." American Journal of Undergraduate Research, no. 22 (June 30, 2025): 49–68. https://doi.org/10.33697/ajur.2025.141.

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There has been a growing number of datasets exhibiting an excess of zero values that cannot be adequately modeled using standard probability distributions. For example, microbiome data and single-cell RNA sequencing data consist of count measurements in which the proportion of zeros exceeds what can be captured by standard distributions such as the Poisson or negative binomial, while also requiring appropriate modeling of the nonzero counts. Several models have been proposed to address zero-inflated datasets including the zero-inflated negative binomial, hurdle negative binomial model, and the

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Simmachan, Teerawat, Noppachai Wongsai, Sangdao Wongsai, and Rattana Lerdsuwansri. "Modeling road accident fatalities with underdispersion and zero-inflated counts." PLOS ONE 17, no. 11 (2022): e0269022. http://dx.doi.org/10.1371/journal.pone.0269022.

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In 2013, Thailand was ranked second in the world in road accident fatalities (RAFs), with 36.2 per 100,000 people. During the Songkran festival, which takes place during the traditional Thai New Year in April, the number of road traffic accidents (RTAs) and RAFs are markedly higher than on regular days, but few studies have investigated this issue as an effect of festivity. This study investigated the factors that contribute to RAFs using various count regression models. Data on 20,229 accidents in 2015 were collected from the Department of Disaster Prevention and Mitigation in Thailand. The P

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Maiti, Raju, Atanu Biswas, and Samarjit Das. "Time Series of Zero-Inflated Counts and their Coherent Forecasting." Journal of Forecasting 34, no. 8 (2015): 694–707. http://dx.doi.org/10.1002/for.2368.

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Baetschmann, Gregori, and Rainer Winkelmann. "Modeling zero-inflated count data when exposure varies: With an application to tumor counts." Biometrical Journal 55, no. 5 (2013): 679–86. http://dx.doi.org/10.1002/bimj.201200021.

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LEE, J. H., G. HAN, W. J. FULP, and A. R. GIULIANO. "Analysis of overdispersed count data: application to the Human Papillomavirus Infection in Men (HIM) Study." Epidemiology and Infection 140, no. 6 (2011): 1087–94. http://dx.doi.org/10.1017/s095026881100166x.

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SUMMARYThe Poisson model can be applied to the count of events occurring within a specific time period. The main feature of the Poisson model is the assumption that the mean and variance of the count data are equal. However, this equal mean-variance relationship rarely occurs in observational data. In most cases, the observed variance is larger than the assumed variance, which is called overdispersion. Further, when the observed data involve excessive zero counts, the problem of overdispersion results in underestimating the variance of the estimated parameter, and thus produces a misleading co

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Rumahorbo, Kusni Rohani, Budi Susetyo, and Kusman Sadik. "PEMODELAN DATA TERSENSOR KANAN MENGGUNAKAN ZERO INFLATED NEGATIVE BINOMIAL DAN HURDLE NEGATIVE BINOMIAL." Indonesian Journal of Statistics and Its Applications 3, no. 2 (2019): 184–201. http://dx.doi.org/10.29244/ijsa.v3i2.247.

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Health is a very important thing for humanity. One way to look at a person's health condition is through the number of unhealthy days which can also shows the productivity of the community in a region. Modeling the number of unhealthy days which are examples of count data can be done using Poisson regression. Problems that are often faced in data counts are overdispersion and excess zero. Poisson regression cannot be applied to data that experiences both of these. Zero Inflated Negative Binomial and Hurdle Negative Binomial modeling was performed on data with 2 conditions, uncensored and censo

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Zhang, Xinyan, and Nengjun Yi. "Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data." Bioinformatics 36, no. 8 (2020): 2345–51. http://dx.doi.org/10.1093/bioinformatics/btz973.

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Abstract Motivation Longitudinal metagenomics data, including both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic associations between the human microbiome and various diseases. However, analytic tools have not been fully developed to simultaneously address the main challenges of longitudinal metagenomics data, i.e. high-dimensionality, dependence among samples and zero-inflation of observed counts. Results We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze high-dimensional longitudinal metagen

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Pearce, Michael, and Michael D. Perlman. "Estimating the Ratio of Means in a Zero-Inflated Poisson Mixture Model." Stats 8, no. 3 (2025): 55. https://doi.org/10.3390/stats8030055.

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The problem of estimating the ratio of the means of a two-component Poisson mixture model is considered, when each component is subject to zero-inflation, i.e., excess zero counts. The resulting zero-inflated Poisson mixture (ZIPM) model can be viewed as a three-component Poisson mixture model with one degenerate component. The EM algorithm is applied to obtain frequentist estimators and their standard errors, the latter determined via an explicit expression for the observed information matrix. As an intermediate step, we derive an explicit expression for standard errors in the two-component P

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Simmachan, Teerawat, Noppachai Wongsai, Sangdao Wongsai, and Rattana Lerdsuwansri. "Correction: Modeling road accident fatalities with underdispersion and zero-inflated counts." PLOS ONE 19, no. 8 (2024): e0309234. http://dx.doi.org/10.1371/journal.pone.0309234.

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Abiodun, Gbenga J., Olusola S. Makinde, Abiodun M. Adeola, et al. "A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa." International Journal of Environmental Research and Public Health 16, no. 11 (2019): 2000. http://dx.doi.org/10.3390/ijerph16112000.

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Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box–Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box–Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box–Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative bin

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Rakitzis, Athanasios C., Philippe Castagliola, and Petros E. Maravelakis. "Cumulative sum control charts for monitoring geometrically inflated Poisson processes: An application to infectious disease counts data." Statistical Methods in Medical Research 27, no. 2 (2016): 622–41. http://dx.doi.org/10.1177/0962280216641985.

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In this work, we study upper-sided cumulative sum control charts that are suitable for monitoring geometrically inflated Poisson processes. We assume that a process is properly described by a two-parameter extension of the zero-inflated Poisson distribution, which can be used for modeling count data with an excessive number of zero and non-zero values. Two different upper-sided cumulative sum-type schemes are considered, both suitable for the detection of increasing shifts in the average of the process. Aspects of their statistical design are discussed and their performance is compared under v

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Kim, Minwoo, Himchan Jeong, and Dipak Dey. "Approximation of Zero-Inflated Poisson Credibility Premium via Variational Bayes Approach." Risks 10, no. 3 (2022): 54. http://dx.doi.org/10.3390/risks10030054.

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While both zero-inflation and the unobserved heterogeneity in risks are prevalent issues in modeling insurance claim counts, determination of Bayesian credibility premium of the claim counts with these features are often demanding due to high computational costs associated with a use of MCMC. This article explores a way to approximate credibility premium for claims frequency that follows a zero-inflated Poisson distribution via variational Bayes approach. Unlike many existing industry benchmarks, the proposed method enables insurance companies to capture both zero-inflation and unobserved hete

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Tang, Zheng-Zheng, and Guanhua Chen. "Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis." Biostatistics 20, no. 4 (2018): 698–713. http://dx.doi.org/10.1093/biostatistics/kxy025.

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Summary There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes th

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Liu, Xueyan, Bryan Winter, Li Tang, Bo Zhang, Zhiwei Zhang, and Hui Zhang. "Simulating comparisons of different computing algorithms fitting zero-inflated Poisson models for zero abundant counts." Journal of Statistical Computation and Simulation 87, no. 13 (2017): 2609–21. http://dx.doi.org/10.1080/00949655.2017.1327590.

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Ziadinov, I., P. Deplazes, A. Mathis, et al. "Frequency Distribution Of Echinococcus Multilocularis And Other Helminths Of Foxes In Kyrgyzstan." Veterinary parasitology 171, no. 4-Mar (2010): 286–92. https://doi.org/10.1016/j.vetpar.2010.04.006.

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Echinococcosis is a major emerging zoonosis in central Asia. A study of the helminth fauna of foxes from Naryn Oblast in central Kyrgyzstan was undertaken to investigate the abundance of Echinococcus multilocularis in a district where a high prevalence of this parasite had previously been detected in dogs. A total of 151 foxes (Vulpes vulpes) were investigated in a necropsy study. Of these 96 (64%) were infected with E. multilocularis with a mean abundance of 8669 parasites per fox. This indicates that red foxes are a major definitive host of E. multilocularis in this country. This also demons

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Bermúdez, Lluís, Dimitris Karlis, and Isabel Morillo. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models." Risks 8, no. 1 (2020): 10. http://dx.doi.org/10.3390/risks8010010.

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When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical re

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Zhang, Xinyan, Boyi Guo, and Nengjun Yi. "Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data." PLOS ONE 15, no. 11 (2020): e0242073. http://dx.doi.org/10.1371/journal.pone.0242073.

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Motivation The human microbiome is variable and dynamic in nature. Longitudinal studies could explain the mechanisms in maintaining the microbiome in health or causing dysbiosis in disease. However, it remains challenging to properly analyze the longitudinal microbiome data from either 16S rRNA or metagenome shotgun sequencing studies, output as proportions or counts. Most microbiome data are sparse, requiring statistical models to handle zero-inflation. Moreover, longitudinal design induces correlation among the samples and thus further complicates the analysis and interpretation of the micro

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Xu, Tao, Guangjin Zhu, and Shaomei Han. "Study of depression influencing factors with zero-inflated regression models in a large-scale population survey." BMJ Open 7, no. 11 (2017): e016471. http://dx.doi.org/10.1136/bmjopen-2017-016471.

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ObjectivesThe number of depression symptoms can be considered as count data in order to get complete and accurate analyses findings in studies of depression. This study aims to compare the goodness of fit of four count outcomes models by a large survey sample to identify the optimum model for a risk factor study of the number of depression symptoms.Methods15 820 subjects, aged 10 to 80 years old, who were not suffering from serious chronic diseases and had not run a high fever in the past 15 days, agreed to take part in this survey; 15 462 subjects completed all the survey scales. The number o

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Lee, Kyu Ha, Brent A. Coull, Anna-Barbara Moscicki, Bruce J. Paster, and Jacqueline R. Starr. "Bayesian variable selection for multivariate zero-inflated models: Application to microbiome count data." Biostatistics 21, no. 3 (2018): 499–517. http://dx.doi.org/10.1093/biostatistics/kxy067.

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Summary Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses. Analysis of microbial count data also requires special attention

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Li, Cong, Shuai Cui, and Dehui Wang. "Monitoring the Zero-Inflated Time Series Model of Counts with Random Coefficient." Entropy 23, no. 3 (2021): 372. http://dx.doi.org/10.3390/e23030372.

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In this research, we consider monitoring mean and correlation changes from zero-inflated autocorrelated count data based on the integer-valued time series model with random survival rate. A cumulative sum control chart is constructed due to its efficiency, the corresponding calculation methods of average run length and the standard deviation of the run length are given. Practical guidelines concerning the chart design are investigated. Extensive computations based on designs of experiments are conducted to illustrate the validity of the proposed method. Comparisons with the conventional contro

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Neelon, Brian, and Dongjun Chung. "The LZIP: A Bayesian latent factor model for correlated zero-inflated counts." Biometrics 73, no. 1 (2016): 185–96. http://dx.doi.org/10.1111/biom.12558.

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Wang, Craig, Paul R. Torgerson, Johan Höglund, and Reinhard Furrer. "Zero-inflated hierarchical models for faecal egg counts to assess anthelmintic efficacy." Veterinary Parasitology 235 (February 2017): 20–28. http://dx.doi.org/10.1016/j.vetpar.2016.12.007.

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Mirkamali, Sayed Jamal, and Mojtaba Ganjali. "A general location model with zero-inflated counts and skew normal outcomes." Journal of Applied Statistics 44, no. 15 (2016): 2716–28. http://dx.doi.org/10.1080/02664763.2016.1261813.

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Chiang, Jyun-You, Yuhlong Lio, Chien-Ya Hsu, Chia-Ling Ho, and Tzong-Ru Tsai. "Binary Classification with Imbalanced Data." Entropy 26, no. 1 (2023): 15. http://dx.doi.org/10.3390/e26010015.

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When the binary response variable contains an excess of zero counts, the data are imbalanced. Imbalanced data cause trouble for binary classification. To simplify the numerical computation to obtain the maximum likelihood estimators of the zero-inflated Bernoulli (ZIBer) model parameters with imbalanced data, an expectation-maximization (EM) algorithm is proposed to derive the maximum likelihood estimates of the model parameters. The logistic regression model links the Bernoulli probabilities with the covariates in the ZIBer model, and the prediction performance among the ZIBer model, LightGBM

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TRUONG, BUU-CHAU, KIM-HUNG PHO, CONG-CHANH DINH, and MICHAEL McALEER. "ZERO-INFLATED POISSON REGRESSION MODELS: APPLICATIONS IN THE SCIENCES AND SOCIAL SCIENCES." Annals of Financial Economics 16, no. 02 (2021): 2150006. http://dx.doi.org/10.1142/s2010495221500068.

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This paper makes a theoretical contribution by presenting a detailed derivation of a zero-inflated Poisson (ZIP) model, and then deriving the parameters of the ZIP model using a fishing data set. This model has several practical applications, and is largely performed to model count data that have an excess number of zero counts. In the scope of the paper, we introduce the complete formulae, the likelihood and log-likelihood functions and the estimating equation of the ZIP model. We then investigate the theory of large sample properties of this model under some regularity conditions. A simulati

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Alomair, Gadir. "Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset." PLOS ONE 19, no. 12 (2024): e0314975. https://doi.org/10.1371/journal.pone.0314975.

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Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricing policies. Traditional methods typically rely on a Poisson distribution for modeling claim counts; however, this approach can be inadequate due to frequent zero-claim periods, leading to zero inflation in the data. Zero inflation occurs when more zeros are observed than expected under standard Poisson or negative binomial (NB) models. While machine learning (ML) techniques have been explored for predictive analytics in other contexts, their appl

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