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Dissertations / Theses on the topic 'Zero-Inflated counts'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

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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2

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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3

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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5

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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7

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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8

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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9

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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10

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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11

Fan, Huihao. "Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513.

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12

Yang, Hui. "Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1452167047.

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13

Kreider, Scott Edwin Douglas. "A case study in handling over-dispersion in nematode count data." Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4248.

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14

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 c

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15

Dartnall, James Edward. "Examining the effect of daylight on road accidents and investigating a state space time series approach to modelling zero inflated count data." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438672.

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16

Llorens, Aleixandre Noelia. "Evaluación en el modelado de las respuestas de recuento." Doctoral thesis, Universitat de les Illes Balears, 2005. http://hdl.handle.net/10803/9446.

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Este trabajo presenta dos líneas de investigación desarrolladas en los últimos años en torno a la etapa de evaluación en datos de recuento. Los campos de estudio han sido: los datos de recuento, concretamente el estudio del modelo de regresión de Poisson y sus extensiones y la etapa de evaluación como punto de inflexión en el proceso de modelado estadístico. Los resultados obtenidos ponen de manifiesto la importancia de aplicar el modelo adecuado a las características de los datos así como de evaluar el ajuste del mismo. Por otra parte la comparación de pruebas, índices, estimadores y modelos

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17

Garden, Cheryl Ellen. "Modeling zero inflated count data." Thesis, 1996. http://hdl.handle.net/2429/4495.

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A natural approach to analyzing the effect of covariates on a count response variable is to use a Poisson regression model. A complication is that the counts are often more variable than can be explained by a Poisson model. This problem, referred to as overdispersion, has received a great deal of attention in recent literature and a number of variations on the Poisson regression model have been developed. As such, statistical consultants are faced with the difficult task of identifying which of these alternative models is best suited to their particular application. In this thesis, two a

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18

Dobbie, Melissa Jane. "Modelling correlated zero-inflated count data." Phd thesis, 2001. http://hdl.handle.net/1885/147622.

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19

Hsu, Yu-Ling, and 許祐領. "Robust Inference for variance function of zero-inflated count responses." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/74970922220732267341.

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碩士<br>國立中央大學<br>統計研究所<br>100<br>(一) zero-inflated and over-dispersed count data are encountered in many research areas. Such data are generally more difficult to analyze due to the scarcity of appropriate statistic models. We demonstrate that the normal model can be easily modified to provide asymptotically legitimate likelihood inference about the parameters for in the variance function . Contrast is also made with models proposed in the literatures.

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20

Ghanney, Bartholomew Embir. "Estimation of zero-inflated count time series models with and without covariates." 2015. http://hdl.handle.net/1993/30920.

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Zero inflation occurs when the proportion of zeros of a model is greater than the proportion of zeros of the corresponding Poisson model. This situation is very common in count data. In order to model zero inflated count time series data, we propose the zero inflated autoregressive conditional Poisson (ZIACP) model by the extending the autoregressive conditional poisson (ACP) model of Ghahramani and Thavaneswaran (2009). The stationarity conditions and the autocorrelation functions of the ZIACP model are provided. Based on the expectation maximization (EM) algorithm an estimation method is dev

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21

Wang, Lijuan. "Generalized mixed models with mixture links for multivariate zero-inflated count data." 2008. http://wwwlib.umi.com/dissertations/fullcit/3362903.

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22

Mawella, Nadeesha R. "A robust test of homogeneity in zero-inflated models for count data." Diss., 2018. http://hdl.handle.net/2097/38877.

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Doctor of Philosophy<br>Department of Statistics<br>Wei-Wen Hsu<br>Evaluating heterogeneity in the class of zero-inflated models has attracted considerable attention in the literature, where the heterogeneity refers to the instances of zero counts generated from two different sources. The mixture probability or the so-called mixing weight in the zero-inflated model is used to measure the extent of such heterogeneity in the population. Typically, the homogeneity tests are employed to examine the mixing weight at zero. Various testing procedures for homogeneity in zero-inflated models, such as s

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23

Mamun, Md Abdullah Al. "Zero-inflated regression models for count data : an application to under-5 deaths." 2014. http://liblink.bsu.edu/uhtbin/catkey/1747408.

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Zero-inflated (ZI) count data models overcome the restriction of equality relationship between mean and variance, but functional relationship still exists. For ZI models it is important to know whether the proportion of zeros and the rate of counts have any influence on the fit of the model. In this study we have considered three zero-inflated models, namely, ZIP, ZINB, and Hurdle model. We also considered Poisson and negative binomial model as classical count data models. Our simulation experiment suggests that the proportion of zeros for given rate parameter does not a↵ect the fit of the mod

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24

Sung, Chin-Hsiung, and 宋志雄. "Evaluation of Parameter Estimations in Log-Linear Model under Zero-Inflated Count Data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/46545339679568069406.

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碩士<br>國立臺北大學<br>統計學系<br>103<br>More and more customers use credit cards or electronic purse to pay their bills instead of real money. Quite frequently, people have more than one credit card on average. Nevertheless, only a few credit cards are used. To analyze the consumer behavior in using credit cards, there exists many zeros. Such a data with many zeros are called zero-inflated count data. To deal with the excess zeros, Lambert (1992) proposed a zero-inflated Poisson distribution. The most popular model for consumer consumption behavior was proposed by Ehrenberg (1959) which is called the p

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25

Rodrigues-Motta, Mariana. "Zero-inflated poisson models for quantitative genetic analysis of count data with applications to mastitis in dairy cows." 2006. http://www.library.wisc.edu/databases/connect/dissertations.html.

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26

Bhattacharya, Archan. "Inference for controlled branching process, Bayesian inference for zero-inflated count data and Bayesian techniques for hairline fracture detection and reconstruction." 2007. http://purl.galileo.usg.edu/uga%5Fetd/bhattacharya%5Farchan%5F200705%5Fphd.

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27

Yu, Kuan Yi, and 余冠毅. "Some discussions on the performance of parameter estimations of the log-linear model and its extended models under the zero-inflated count data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/72534318388068624846.

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碩士<br>國立臺北大學<br>統計學系<br>103<br>The most common parametric assumption for analyzing count data is Poisson distribution. However, it constructs under the assumption that the data have features that the mean equals variance. Nowadays, owing to there rapid development in technology storage, data are abundant and come from many different sources. In turn, the data no longer have the feature that the mean equals variance. Adding a new dispersion parameter in Poisson distribution, Consul and Jain (1970) proposed the generalized Poisson distribution. Mullahy (1986) suggested combining the Bernoulli an

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28

Rivest, Amélie. "La régression de Poisson multiniveau généralisée au sein d’un devis longitudinal : un exemple de modélisation du nombre d’arrestations de membres de gangs de rue à Montréal entre 2005 et 2007." Thèse, 2012. http://hdl.handle.net/1866/9924.

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Les données comptées (count data) possèdent des distributions ayant des caractéristiques particulières comme la non-normalité, l’hétérogénéité des variances ainsi qu’un nombre important de zéros. Il est donc nécessaire d’utiliser les modèles appropriés afin d’obtenir des résultats non biaisés. Ce mémoire compare quatre modèles d’analyse pouvant être utilisés pour les données comptées : le modèle de Poisson, le modèle binomial négatif, le modèle de Poisson avec inflation du zéro et le modèle binomial négatif avec inflation du zéro. À des fins de comparaisons, la prédiction de la proportion du z

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