Relevant bibliographies by topics / Zero-Inflated counts

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Zero-Inflated counts'

Author: Grafiati
Published: 28 June 2021
Last updated: 5 February 2022

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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 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts.
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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 (September 21, 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 binomial mixed effects model (ZINBMM) to address excessive zero counts in the NOD and OD RNA-seq data respectively in the presence of random effects. We apply these methods to both simulated and real RNA-seq datasets. The ZIPMM and ZINBMM perform better on both simulated and real datasets.
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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 multilevel zero-inflated Poisson (MZIP) model and zero-inflated negative binomial (ZINB) and zero-inflated Poisson (ZIP) models.
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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 (October 25, 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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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 (February 1, 2015): 52–60. http://dx.doi.org/10.3844/jmssp.2015.52.60.

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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 (February 20, 2012): 878–85. http://dx.doi.org/10.1111/j.1541-0420.2011.01729.x.

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Cantoni, Eva, and Marie Auda. "Stochastic variable selection strategies for zero-inflated models." Statistical Modelling 18, no. 1 (June 30, 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 chain of interesting models. As an additional novelty, we propose three ways of extracting information from this rich chain and we compare them in two simulation studies, where we also contrast our approach with regularization (penalized) techniques available in the literature. The analysis of a typical dataset that has motivated our research is also presented, before concluding with some recommendations.
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Jang, Jong-Hwan, Junggu Choi, Hyun Woong Roh, Sang Joon Son, Chang Hyung Hong, Eun Young Kim, Tae Young Kim, and Dukyong Yoon. "Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study." JMIR mHealth and uHealth 8, no. 7 (July 23, 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 approach. Methods To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning–based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning–based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression). Results The zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts. Conclusions Our deep learning–based imputation model performed better than the other methods when imputing missing values in actigraphy data.
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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 (September 30, 2015): 694–707. http://dx.doi.org/10.1002/for.2368.

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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 (July 14, 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 Monte Carlo methods to estimate the parameters of the zero-inflated negative binomial distribution. The analysis of 3000 simulated data sets indicated that this method out-performed the maximum likelihood procedure. Application of this technique to faecal egg counts from lambs in a commercial upland flock indicated thatN. battuscounts were indeed zero-inflated. Estimating the extent of zero-inflation is important for effective statistical analysis and for the accurate identification of genetically resistant animals.
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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 simulations in R.
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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
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 this context, the aim of this thesis was to increase the knowledge about wind farms impacts on avifauna, which variables influence birds’ fatalities by collision with wind turbines and birds’ vulnerability. Models based on excessive zero counts were tested to understand which variables influence birds’ fatalities assessed on 25 Portuguese wind farms. This allowed to estimate the probability of mortality observation per species. The information obtained was used to build the fatality risk index that also considered the vulnerability factors, which give information of species conservation concern and resilience. Those indexes allow to prioritise the existing and limited conservation efforts on more vulnerable species. Models and indexes are also important for improving knowledge about wind energy impacts on wildlife and what can lead to reduce them, in order to achieve a sustainable and greener future.
As alterações climáticas são uma das maiores ameaças para a Humanidade e para a vida selvagem. A consciência sobre a importância destas questões motivou a procura de alternativas, com intuito de mitigar estas alterações globais, causadas nomeadamente pelos gases de efeitos de estufa. Assim, as energias renováveis apresentam-se como uma possível estratégia vencedora a adotar, de forma a reduzir as emissões destes gases e levar à independência energética. No entanto, o uso destas energias renováveis ainda apresenta impactes negativos, especialmente para os ecossistemas. A energia eólica é inclusivamente considerada uma das maiores causas não intencionais de origem antropogénica para a mortalidade adicional de aves. Neste contexto, esta dissertação tem como os principais objetivos o desenvolvimento do conhecimento relativo aos impactes da energia eólica, quais as variáveis que influenciam a mortalidade de aves respeitante à colisão com as turbinas eólicas assim como as variáveis que afetam a vulnerabilidade das espécies. Foram testados modelos de contagem com excesso de zeros para compreender a influência das variáveis nas observações de mortalidade em 25 parques eólicos portugueses. A partir destes modelos foi possível estimar a probabilidade de observação de mortalidade para cada uma das espécies estudadas, provocada por colisão com eólicas. Esta informação foi ainda utilizada de forma a desenvolver um índice de risco de fatalidade com base nestas estimativas, assim como em fatores elucidativos da vulnerabilidade das espécies, nomeadamente o seu estatuto de conservação e resiliência. Desta forma é então possível direcionar esforços e recursos para a preservação das espécies com maior vulnerabilidade e prioridade de conservação. Este tipo de modelos e índices é ainda fundamental para incrementar o conhecimento sobre os impactes da energia eólica na vida selvagem e para compreender quais as medidas que podem ser tomadas para os reduzir e, assim, garantir um futuro mais verde e sustentável para todas as formas de vida.
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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 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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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 predictors of the parameters. Similar to the generalized linear models, the GAMLSSs link predictors to parameters through monotonic link functions, which can also change for each parameter. This dissertation describes the GAMLSSs methodology and presents two applications to data sets provenient from experiments in agronomy; exploring methods of estimation, diagnosis and comparison of these models.
Os modelos lineares generalizados para locação, escala e forma (GAMLSS) desenvolvidos por Rigby e Stasinopoulos (2005) são uma ampla classe de modelos de regressão univariados que não pressupõem que a distribuição da variável resposta pertença à família exponencial como os modelos lineares generalizados ou aditivos generalizados, por exemplo. Além do mais, eles permitem que todos os parâmetros da distribuição da variável resposta sejam modelados explicitamente por meio de diferentes conjuntos de variáveis explanatórias. A subclasse semiparamétrica dos GAMLSS, em particular, permite que uma grande variedade de termos paramétricos e não paramétricos sejam incluídos nos preditores dos parâmetros da distribuição assumida para a variável resposta. De forma análoga aos modelos lineares generalizados, os GAMLSSs ligam os preditores aos parâmetros por meio de funções de ligação monótonas, que também podem mudar de acordo com o parâmetro a ser estimado. Esta dissertação descreve a metodologia dos modelos lineares generalizados para locação, escala e forma e apresenta duas aplicações a bancos de dados provenientes de experimentos agrícolas; explorando métodos de estimação, diagnóstico e comparação desse tipo de modelos.
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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 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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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
Department of Statistics
Wei-Wen Hsu
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 parameters but curvilinear or nonlinear. Under such situation, a more general and flexible approach should be considered. One popular method in the literature is Zero-Inflated Generalized Additive Models (ZIGAM) which extends the zero-inflated models to incorporate the use of Generalized Additive Models (GAM). These models can accommodate the nonlinear predictor in the link function. For ZIGAM, it is also of interest to conduct inferences for the mixing weight, particularly evaluating whether the mixing weight equals to zero. Many methodologies have been proposed to examine this question, but all of them are developed under classical zero-inflated models rather than ZIGAM. In this report, we propose a generalized score test to evaluate whether the mixing weight is equal to zero under the framework of ZIGAM with Poisson model. Technically, the proposed score test is developed based on a novel transformation for the mixing weight coupled with proportional constraints on ZIGAM, where it assumes that the smooth components of covariates in both the Poisson mean and the mixing weight have proportional relationships. An intensive simulation study indicates that the proposed score test outperforms the other existing tests when the mixing weight and the Poisson mean truly involve a nonlinear predictor. The recreational fisheries data from the Marine Recreational Information Program (MRIP) survey conducted by National Oceanic and Atmospheric Administration (NOAA) are used to illustrate the proposed methodology.
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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. Ahmedabad: Indian Institute of Management, 2013.

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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 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, 225–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03915-7_20.

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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, 261–93. New York, NY: 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, 550–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_40.

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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 is designed to close this gap and provide an approach that could be helpful to those applying control charts in such cases.
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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, 68–88. 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 locations. In particular, longer travel and worse economy conditions, such as low employment and average annual income and more population under poverty, may prevent more movements, which have more impacts during peak hours. A better transit system may reduce the taxi trips, except for areas with more subway stations. Develpoed area for instance more commercial or residential area is more likely to attract more visits by taxis, as well as dense public facilities but with more temporal variations.
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Conference papers on the topic "Zero-Inflated counts"

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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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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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