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Relevant bibliographies by topics / Overdispersion

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Academic literature on the topic 'Overdispersion'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Overdispersion"

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Amarita, Isma, and Nusar Hajarisman. "Penerapan Model Regresi Zero Inflated Negative Binomial pada Kasus Campak di Provinsi Jawa Barat Tahun 2020." Bandung Conference Series: Statistics 3, no. 2 (2023): 737–44. http://dx.doi.org/10.29313/bcss.v3i2.9311.

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Abstrak. Analisis regresi merupakan suatu metode yang digunakan untuk mengetahui hubungan antara variabel bebas dan variabel respon. Dalam sebuah analisis regresi dengan variabel respon yang bersifat diskrit, dapat menggunakan analisis regresi Poisson. Pada regresi Poisson harus memenuhi asumsi equisdispersi. Namun dalam pengaplikasiannya tak jarang mengalami pelanggaran asumsi, dimana nilai varians lebih besar dari nilai rata – ratanya atau bisa disebut dengan overdispersi. Salah satu penyebab overdispersi adalah adanya nilai nol yang berlebih (excess zeros) pada data variabel respon. Metode

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Aini, Zahra Tiara, and Anneke Iswani Achmad. "Penerapan Regresi Binomial Negatif dalam Memodelkan Angka Kelahiran Remaja Usia 15-19 Tahun di Indonesia pada Tahun 2017." Bandung Conference Series: Statistics 2, no. 2 (2022): 87–95. http://dx.doi.org/10.29313/bcss.v2i2.3233.

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Abstract. Poisson regression is a method to analyze the relationship between the independent variable and the dependent variable, which is discrete. In Poisson regression, it must meet the assumption of equidispersion, namely the assumption that the variance and average values of the data are the same. However, discrete data often experiences overdispersion conditions, namely a situation where the variance value is greater than the average. A good alternative regression model for data experiencing overdispersion conditions is a negative binomial regression model that can model data experiencin

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Indraswari, Salsabila Putri, and Antik Suprihanti. "Laney P' Chart Effectiveness in Quality Control of Cigar Production." Industria: Jurnal Teknologi dan Manajemen Agroindustri 13, no. 2 (2024): 140–51. https://doi.org/10.21776/ub.industria.2024.013.02.2.

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Abstract This study aimed to evaluate the effectiveness of Laney p' chart in overcoming the limitations of conventional p-chart in cigar quality control, especially in handling overdispersion of production data. Overdispersion often occurs in agricultural processes with large sample sizes, resulting in narrow control limits and false alarms. The study was conducted at PT Taru Martani, using cigar quality data from three main production units from August 2021 to July 2022. A quantitative descriptive approach was used to analyze the proportion of product defects. Initial analysis with convention

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Ganio, Lisa M., and Daniel W. Schafer. "Diagnostics for Overdispersion." Journal of the American Statistical Association 87, no. 419 (1992): 795–804. http://dx.doi.org/10.1080/01621459.1992.10475281.

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Noviana, Irna, and Nur Azizah Komara Rifai. "Penerapan Generalized Poisson Regression (GPR) dalam Memodelkan Kasus Campak pada Balita di Kabupaten Bandung Tahun 2020." Bandung Conference Series: Statistics 3, no. 2 (2023): 200–209. http://dx.doi.org/10.29313/bcss.v3i2.7850.

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Abstract. Poisson regression is a regression method used to analyze count data with Poisson distributed response variables. In Poisson regression, there is an assumption that the mean value of the response variable must be equal to the variance value. If that assumption is not met, for example there is an overdispersion case where the variance value is greater than the average value and that is left unaddressed, making the standard error value of the estimated regression parameter tend to be lower than the supposed value (underestimate) resulting in a less accurate test conclusion. In this stu

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Rana, Sohel, Abu Sayed Md Al Mamun, FM Arifur Rahman, and Hanaa Elgohari. "Outliers as a Source of Overdispersion in Poisson Regression Modelling: Evidence from Simulation and Real Data." International Journal of Statistical Sciences 23, no. 2 (2023): 31–37. http://dx.doi.org/10.3329/ijss.v23i2.70105.

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The Poisson regression model is a well-known technique for modelling count data. However, it is necessary to satisfy the overdispersion assumption in order to fit the Poisson regression model. Due to the overdispersion problem in the Poisson regression model, standard errors might be underestimated, which may lead to a highly misleading inference. There are several tests in the literature to check the presence of overdispersion in the Poisson model. In this study, we apply a regression-based t test to identify the overdispersion. The simulation study and real data example clearly show that the

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Rahayu, Ayu. "Model-Model Regresi untuk Mengatasi Masalah Overdipersi pada Regresi Poisson." Journal Peqguruang: Conference Series 2, no. 1 (2021): 1. http://dx.doi.org/10.35329/jp.v2i1.1866.

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Model Regresi Poisson merupakan model standar yang digunakan untuk menganalisis data yang memuat variabel dependen berupa diskrit (count data). Pada regresi Poisson terdapat asumsi yang harus dipenuhi yaitu kesamaan antara nilai mean dan variansinya. Akan tetapi, pada analisis data diskrit yang menggunakan regresi Poisson sering terjadi overdispersi (overdispersion) yaitu keadaan nilai variansnya lebih besar dari nilai meannya. Salah satu penyebab terjadinya overdispersi adalah terdapat kelebihan nilai nol pada variabel dependennya. Adanya overdispersi dalam data menyebabkan nilai prediksi men

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

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In this study, we examine the influence of overdispersion on statistical inference in a zero-inflated generalized Poisson regression model by simulation experiments and real data analysis. In the simulation study, simulated data are generated from the zero-inflated generalized Poisson regression model, and the regression coefficients in the zero-inflated Poisson regression and the zero-inflated generalized Poisson regression models are estimated. The simulation experiment results show that the regression coefficient estimates for the mean and zero-inflation probability of the zero-inflated Poi

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Rahayu, Lili Puspita, Kusman Sadik, and Indahwati Indahwati. "Overdispersion study of poisson and zero-inflated poisson regression for some characteristics of the data on lamda, n, p." International Journal of Advances in Intelligent Informatics 2, no. 3 (2016): 140. http://dx.doi.org/10.26555/ijain.v2i3.73.

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Poisson distribution is one of discrete distribution that is often used in modeling of rare events. The data obtained in form of counts with non-negative integers. One of analysis that is used in modeling count data is Poisson regression. Deviation of assumption that often occurs in the Poisson regression is overdispersion. Cause of overdispersion is an excess zero probability on the response variable. Solving model that be used to overcome of overdispersion is zero-inflated Poisson (ZIP) regression. The research aimed to develop a study of overdispersion for Poisson and ZIP regression on some

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Adwendi, Satria June, Asep Saefuddin, and Budi Susetyo. "A STUDY OF SMALL AREA ESTIMATION TO MEASURE MULTIDIMENSIONAL POVERTY WITH MIXED MODEL POISSON, ZIP, AND ZINB." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (2023): 0439–48. http://dx.doi.org/10.30598/barekengvol17iss1pp0439-0448.

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The research began with calculating the value of multidimensional poverty at the district level in West Java Province from SUSENAS 2021. The calculation of multidimensional poverty was based on individuals in each district or city household. The dimensional weights are weighed the same, and the indicators in the dimensions are also weighed the same. Furthermore, the simulation study used the Poisson, ZIP, and ZINB mixed models to examine the model's performance on data with cases of excess zero values and overdispersion. The simulation was by generating data without overdispersion and with ove

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Dissertations / Theses on the topic "Overdispersion"

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Crouchley, Robert. "Testing for overdispersion in the parametric proportional hazards and some related models." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403689.

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Zheng, Shuo. "New Hierarchical Nonlinear Modeling for Count Data: Estimation and Testing in The Presence of Overdispersion." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/132533.

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Statistics<br>Ph.D.<br>In studies of traffic accidents, disease occurrence, mismatches in genetic code and impact of pollution on ecological communities the key observational variable is often a count. For example, daily counts of accidents on a segment of highway will vary with weather and other traffic conditions: highway engineers seek to relate accident counts to these roadway conditions. Formal statistical analysis of this kind of data is critically dependent on using the correct model for the random count data. However, statistical analysis packages use only those random count models tha

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Davidson, Christopher L. "A Model Selection Paradigm for Modeling Recurrent Adenoma Data in Polyp Prevention Trials." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/228465.

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Colorectal polyp prevention trials (PPTs) are randomized, placebo-controlled clinical trials that evaluate some chemo-preventive agent and include participants who will be followed for at least 3 years to compare the recurrence rates (counts) of adenomas. A large proportion of zero counts will likely be observed in both groups at the end of the observation period. Poisson general linear models (GLMs) are usually employed for estimation of recurrence in PPTs. Other models, including the negative binomial (NB2), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) may be bette

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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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Rocha, Everton Batista da. "Modelos para a análise de dados de contagens longitudinais com superdispersão: estimação INLA." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-05112015-144057/.

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Em ensaios clínicos é muito comum a ocorrência de dados longitudinais discretos. Para sua análise é necessário levar em consideração que dados observados na mesma unidade experimental ao longo do tempo possam ser correlacionados. Além dessa correlação inerente aos dados é comum ocorrer o fenômeno de superdispersão (ou sobredispersão), em que, existe uma variabilidade nos dados além daquela captada pelo modelo. Um caso que pode acarretar a superdispersão é o excesso de zeros, podendo também a superdispersão ocorrer em valores não nulos, ou ainda, em ambos os casos. Molenberghs, Verbeke e Demétr

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Silva, Andreza Jardelino da. "Modelos para análise de dados superdispersos de indução de haploidia em milho." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-20072017-133448/.

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O milho é uma espécie alógama cujo produto comercial são os híbridos, os quais originam-se do cruzamento de duas linhagens endogâmicas. Uma forma para obtenção de tais linhagens é por meio das técnicas de indução de haploidia e posterior obtenção dos duplo-haploides, permitindo até 100% de homozigose. Essas técnicas retornam resultados importantes no melhoramento de milho. Uma variável de interesse importante, obtida a partir dessas técnicas é a taxa de indução de haploidia, a qual trata-se de uma proporção entre o número de sementes haploides e o número total de sementes. O conjunto de dados

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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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Oesselmann, Clarissa Cardoso. "Equações de estimação generalizadas com resposta binomial negativa: modelando dados correlacionados de contagem com sobredispersão." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-06072017-122423/.

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Uma suposição muito comum na análise de modelos de regressão é a de respostas independentes. No entanto, quando trabalhamos com dados longitudinais ou agrupados essa suposição pode não fazer sentido. Para resolver esse problema existem diversas metodologias, e talvez a mais conhecida, no contexto não Gaussiano, é a metodologia de Equações de Estimação Generalizadas (EEGs), que possui similaridades com os Modelos Lineares Generalizados (MLGs). Essas similaridades envolvem a classificação do modelo em torno de distribuições da família exponencial e da especificação de uma função de variância. A

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Lora, Mayra Ivanoff. "Modelos de regressão beta-binomial/poisson para contagens bivariadas." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09062011-095707/.

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Propomos um modelo Beta-Binomial/Poisson para dados provenientes de um estudo com doentes de Parkinson, que consistiu em contar durante um minuto quantas tarefas foram realizadas e destas, quantas de maneira correta, antes e depois de um treinamento. O objetivo era verificar se o treinamento aumentava o número de tentativas e a porcentagem de acerto, o que destaca o aspecto bivariado do problema. Esse modelo considera tal aspecto, usa uma distribuição mais adequada a dados de contagem e ainda suporta a sobredispersão presente nos dados. Obtemos estimadores de máxima verossimilhança dos parâmet

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Lora, Mayra Ivanoff. "Modelos Beta-Binomial/Poisson-Gama para contagens bivariadas repetidas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-27082009-120419/.

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Em Lora e Singer (Statistics in Medicine, 2008), propusemos um modelo Beta- Binomial/Poisson p-variado para análise dos dados provenientes de um estudo que consistiu em contar o número de tentativas e acertos de um exercício manual com duração de um minuto realizado por doentes de Parkinson, antes e depois de um treinamento. O objetivo era verificar se o treinamento aumentava o número de tentativas e a porcentagem de acerto, o que destaca o aspecto bivariado do problema. Esse modelo leva tais características em consideração, usa uma distribuição adequada para dados de contagem e ainda acomoda

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Books on the topic "Overdispersion"

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K, Neerchal Nagaraj, and SAS Institute, eds. Overdispersion models in SAS. SAS Institute, 2012.

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David, Fletcher, and Matthew Parry. Overdispersion. Taylor & Francis Group, 2023.

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Staff, Hinde John. Overdispersion Models and Estimation. Taylor & Francis Group, 2004.

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Overdispersion: Models and Estimation (Interdisciplinary Statistics). Chapman & Hall/CRC, 2009.

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Ganio-Gibbons, Lisa M. Diagnostic tools for overdispersion in generalized linear models. 1989.

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Book chapters on the topic "Overdispersion"

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Collett, D. "Overdispersion." In Modelling Binary Data. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-4475-7_6.

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Silva, Maria Eduarda, Isabel Silva, and Cristina Torres. "Modelling Overdispersion with Integer-Valued Moving Average Processes." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28665-1_21.

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Beh, Eric J., and Rosaria Lombardo. "Five Strategies for Accommodating Overdispersion in Simple Correspondence Analysis." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3311-2_10.

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Morales-Otero, Mabel, and Vicente Núñez-Antón. "Bayesian Approaches to Model Overdispersion in Spatio-Temporal Binomial Data." In Contributions to Statistics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65723-8_4.

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Farrington, C. P. "Pearson statistics, goodness of fit, and overdispersion in generalised linear models." In Statistical Modelling. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0789-4_14.

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Nielsen, Bjarke Frost, Kim Sneppen, and Lone Simonsen. "21.1 In Focus: The Impact and Mechanisms of Superspreading." In Principles and Practice of Emergency Research Response. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-48408-7_31.

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AbstractOne of the characteristic features of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is its tendency toward superspreading, where most onward transmission is linked to relatively few of those infected. Indeed, it has been shown that just around 10% of positive individuals account for 80% of new infections. The data necessary to quantify this superspreading tendency were available quite early, even before the outbreak was declared a pandemic. At the time, the epidemiological consequences of superspreading were not well understood, and mathematical models used for forecasti

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Feng, Cindy Xin, and Longhai Li. "Modeling Zero Inflation and Overdispersion in the Length of Hospital Stay for Patients with Ischaemic Heart Disease." In Advanced Statistical Methods in Data Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2594-5_3.

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Jenkins, Phillip H., James F. Burkhart, and Carl J. Kershner. "Corrections for Overdispersion Due to Correlated Counts in Radon Measurements Using Grab Scintillation Cells, Activated Charcoal Devices, and Liquid Scintillation Charcoal Devices." In ACS Symposium Series. American Chemical Society, 2006. http://dx.doi.org/10.1021/bk-2007-0945.ch018.

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"Overdispersion." In Modelling Binary Data. Chapman and Hall/CRC, 2002. http://dx.doi.org/10.1201/b16654-9.

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Lindsey, J. K. "Overdispersion." In Models for Repeated Measurements. Oxford University PressOxford, 1999. http://dx.doi.org/10.1093/oso/9780198505594.003.0007.

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Abstract Categorical data are produced when the response is an indicator of which of a number of events has occurred. However, these will be repeated measurements only if repeated events are observed on the same units (Section 1.2). When no explanatory variables, not even time, distinguish such responses on a unit, the events can be aggregated as counts, a special case of categorical data. This will always occur if only one category of event is being observed. In the simple case of counts, the data will often be collapsed into a contingency table. However, this does not mean that all contingen

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Conference papers on the topic "Overdispersion"

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Uraibi, Hassan S., and Mohammed H. Neamah. "Overdispersion score tests." In 2ND INTERNATIONAL CONFERENCE ON ENGINEERING AND SCIENCE TO ACHIEVE THE SUSTAINABLE DEVELOPMENT GOALS. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0200441.

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Großmann, Gerrit, Michael Backenköhler, and Verena Wolf. "Epidemic overdispersion strengthens the effectiveness of mobility restrictions." In HSCC '21: 24th ACM International Conference on Hybrid Systems: Computation and Control. ACM, 2021. http://dx.doi.org/10.1145/3447928.3457209.

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Beneš, Viktor, Blažena Frcalová, Daniel Klement, et al. "Overdispersion in the Place Cell Discharge—Stochastic Modelling and Inference." In COLLECTIVE DYNAMICS: TOPICS ON COMPETITION AND COOPERATION IN THE BIOSCIENCES: A Selection of Papers in the Proceedings of the BIOCOMP2007 International Conference. AIP, 2008. http://dx.doi.org/10.1063/1.2965086.

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Ostrove, Corey, Erik Nielsen, Kevin Young, and Robin Blume-Kohout. "Out-of-Model Effects and Overdispersion in Gate Set Tomography." In Proposed for presentation at the APS March Meeting held March 14-18, 2022 in Chicago, IL United States. US DOE, 2022. http://dx.doi.org/10.2172/2001887.

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

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Hartono, Powell Gian, Georgina Maria Tinungki, Jakaria Jakaria, Agus Budi Hartono, Patrick Gunawan Hartono, and Richy Wijaya. "Overcoming Overdispersion on Direct Mathematics Learning Model Using the Quasi Poisson Regression." In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.102.

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Saputro, Dewi Retno Sari, Ade Susanti, and Nafisa Berliana Indah Pratiwi. "The handling of overdispersion on Poisson regression model with the generalized Poisson regression model." In THE THIRD INTERNATIONAL CONFERENCE ON MATHEMATICS: Education, Theory and Application. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040330.

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Stewart, C. Lance, Gary E. Stinchcomb, Elizabeth A. Hasenmueller, and Steven L. Forman. "DIGITAL IMAGE ANALYSIS AS A TOOL TO EXPLORE OSL OVERDISPERSION AND SEDIMENT IN THIN SECTION." In GSA Annual Meeting in Denver, Colorado, USA - 2016. Geological Society of America, 2016. http://dx.doi.org/10.1130/abs/2016am-278587.

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Li, Haoran. "Tests for Overdispersion and Zero Inflation in SCED (Single-Case Experimental Design) Count Data: A Multistage Model-Selection Procedure (Poster 7)." In AERA 2024. AERA, 2024. http://dx.doi.org/10.3102/ip.24.2104298.

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Li, Haoran. "Tests for Overdispersion and Zero Inflation in SCED (Single-Case Experimental Design) Count Data: A Multistage Model-Selection Procedure (Poster 7)." In 2024 AERA Annual Meeting. AERA, 2024. http://dx.doi.org/10.3102/2104298.

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Reports on the topic "Overdispersion"

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Bowman, K. O., L. R. Shenton, M. A. Kastenbaum, and K. Broman. Overdispersion: Notes on discrete distributions. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10190987.

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Bowman, K. O., L. R. Shenton, M. A. Kastenbaum, and K. Broman. Overdispersion: Notes on discrete distributions. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7010757.

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Tkachenko, Alexei. Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1659688.

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

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

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