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Journal articles on the topic 'Overdispersion'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

SEKARMINI, NI MADE, I. KOMANG GDE SUKARSA, and I. GUSTI AYU MADE SRINADI. "PENERAPAN REGRESI ZERO-INFLATED NEGATIVE BINOMIAL (ZINB) UNTUK PENDUGAAN KEMATIAN ANAK BALITA." E-Jurnal Matematika 2, no. 4 (2013): 11. http://dx.doi.org/10.24843/mtk.2013.v02.i04.p052.

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One method of regression analysis used to analyze the count data is Poisson regression. Poisson regression requires that the mean value equal to the value of variance (equidispersion). However, sometimes the data is going overdispersion the state variance values ??greater than the mean value. One of the causes overdispersion is the excessive number of zero values ??on the response variable (excess zeros). One method of analysis that can be used on data that had overdispersion due to excess zeros is regression Zero-Inflated Negative Binomial (ZINB). The data that can be analyzed using the ZINB

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12

Luhung Mustika Budiharti and Siti Sunendiari. "Pemodelan dan Pemetaan Jumlah Penderita Kusta di Jawa Barat dengan Regresi Binomial Negatif dan Flexibly Shaped Spatial Scan Statistic." Jurnal Riset Statistika 1, no. 2 (2021): 99–106. http://dx.doi.org/10.29313/jrs.v1i2.409.

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Abstract. Flexibly Shaped Spatial Scan Statistics is a statistical scan method used to detect hotspots in a location in the form of points or aggregates. This method will be more flexible to the shape of the resulting hotspot. The hotspot detection process with this method requires forecast data for each region. The forecast data was obtained with the best modeling between poisson regression and negative binomial regression. In the Poisson regression method there are assumptions that must be met, namely the mean and variance of the response variables must be the same. In fact, in the count dat

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13

Schmidli, Heinz. "Overdispersion Models in SAS." Journal of Biopharmaceutical Statistics 23, no. 3 (2013): 714–15. http://dx.doi.org/10.1080/10543406.2013.756332.

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14

Semkow, Thomas M. "Overdispersion in nuclear statistics." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 422, no. 1-3 (1999): 444–49. http://dx.doi.org/10.1016/s0168-9002(98)01114-0.

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15

Hinde, John, and Clarice G. B. Demétrio. "Overdispersion: Models and estimation." Computational Statistics & Data Analysis 27, no. 2 (1998): 151–70. http://dx.doi.org/10.1016/s0167-9473(98)00007-3.

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16

Berk, Richard, and John M. MacDonald. "Overdispersion and Poisson Regression." Journal of Quantitative Criminology 24, no. 3 (2008): 269–84. http://dx.doi.org/10.1007/s10940-008-9048-4.

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17

PRADAWATI, PUTU SUSAN, KOMANG GDE SUKARSA, and I. GUSTI AYU MADE SRINADI. "PENERAPAN REGRESI BINOMIAL NEGATIF UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON." E-Jurnal Matematika 2, no. 2 (2013): 6. http://dx.doi.org/10.24843/mtk.2013.v02.i02.p031.

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Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From t

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18

Vives, Jaume, Josep-Maria Losilla, Maria-Florencia Rodrigo, and Mariona Portell. "Overdispersion Tests in Count-Data Analysis." Psychological Reports 103, no. 1 (2008): 145–60. http://dx.doi.org/10.2466/pr0.103.1.145-160.

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Count data are commonly assumed to have a Poisson distribution, especially when there is no diagnostic procedure for checking this assumption. However, count data rarely fit the restrictive assumptions of the Poisson distribution. The violation of much of such assumptions commonly results in overdispersion, which invalidates the Poisson distribution. Undetected overdispersion may entail important misleading inferences, so its detection is essential. In this study, different overdispersion diagnostic tests are evaluated through two simulation studies. In Exp. 1, the nominal error rate is compar

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19

Congdon, P. "Approaches to Modelling Overdispersion in the Analysis of Migration." Environment and Planning A: Economy and Space 25, no. 10 (1993): 1481–510. http://dx.doi.org/10.1068/a251481.

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In this paper the modelling of overdispersion in generalised Poisson and multinomial models of migration flows and rates is considered, and its importance within the wider question of substantive model specification is shown. It is argued that substantive specification and the modelling of overdispersion are closely interrelated. Simplified ways of estimating the form of overdispersion—moments methods and pseudo-likelihood—are considered wherever possible. Overdispersion is set within the broader context of correlation effects which relate to migration—correlation across different destinations

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20

Oktaviana, Oktaviana, Wahidah Sanusi, Aswi Aswi, Sukarna Sukarna, and Serifat Adedamola Folorunso. "CONWAY-MAXWELL POISSON REGRESSION MODELING OF INFANT MORTALITY IN SOUTH SULAWESI." MEDIA STATISTIKA 17, no. 1 (2024): 45–56. https://doi.org/10.14710/medstat.17.1.45-56.

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Overdispersion is a common problem in count data that can lead to inaccurate parameter estimates in Poisson regression models. Quasi-Poisson and negative binomial regressions are often used to address overdispersion but have limitations, especially with small samples. The Conway-Maxwell Poisson (CMP) regression model, an extension of the Poisson distribution, effectively addresses both overdispersion and underdispersion, even with limited data, due to additional parameters that better control data dispersion. The Infant Mortality Rate (IMR) is a critical public health indicator, reflecting hea

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21

Valdés-Manuel, José I., and Juan M. Cogollo-Flórez. "Monitoring overdispersed process in clinical laboratories using control charts." DYNA 89, no. 224 (2022): 28–33. http://dx.doi.org/10.15446/dyna.v89n224.103666.

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Overdispersion is a phenomenon that generally occurs in the analysis of large sample sizes. In discrete data analysis, it refers to the presence of a variation higher than that implied by a reference Binomial or Poisson distributions. The proportion of nonconforming units in clinical laboratories presents high variability and, generally, overdispersion. Therefore, it is required to analyze the most appropriate control charts that overcome the limitations of traditional control charts to deal with overdispersed data. This paper performs an analysis of monitoring overdispersed process in clinica

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22

Rizkiah, Adinda Zahrotul, and Nusar Hajarisman. "Pemodelan Hurdle Poisson Regresion pada Jumlah Kasus Kematian Akibat Penyakit HIV/AIDS di Provinsi Jawa Barat." Bandung Conference Series: Statistics 3, no. 2 (2023): 819–27. http://dx.doi.org/10.29313/bcss.v3i2.9487.

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Abstract. To model discrete data related to Poisson events, one way is to use Poisson Regression. If a data contains many zero values, the data can experience overdispersion. This overdispersion problem will increase type I errors, to model the overdispersion data, Hurdle Poisson Regression modeling is needed. Transmission of HIV/AIDS is caused by receiving HIV positive blood donors, through the mother's placenta to her fetus, and sexually transmitted infections. AIDS causes the human body's ability to fight infection to disappear, which can lead to someone's death. However, AIDS-related death

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23

KESWARI, NI MADE RARA, I. WAYAN SUMARJAYA, and NI LUH PUTU SUCIPTAWATI. "PERBANDINGAN REGRESI BINOMIAL NEGATIF DAN REGRESI GENERALISASI POISSON DALAM MENGATASI OVERDISPERSI (Studi Kasus: Jumlah Tenaga Kerja Usaha Pencetak Genteng di Br. Dukuh, Desa Pejaten)." E-Jurnal Matematika 3, no. 3 (2014): 107. http://dx.doi.org/10.24843/mtk.2014.v03.i03.p072.

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Poisson regression is a nonlinear regression that is often used to model count response variable and categorical, interval, or count regressor. This regression assumes equidispersion, i.e., the variance equals the mean. However, in practice, this assumption is often violated. One of this violation is overdispersion in which the variance is greater than the mean. There are several methods to overcome overdispersion. Two of these methods are negative binomial regression and generalized Poisson regression. In this research, binomial negative regression and generalized Poisson regression statistic

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24

Presley, Steven J., Laura M. Cisneros, Christopher L. Higgins, Brian T. Klingbeil, Samuel M. Scheiner, and Michael R. Willig. "Phylogenetic and functional underdispersion in Neotropical phyllostomid bat communities." Biotropica 50, no. 1 (2018): 135–45. https://doi.org/10.5281/zenodo.13435179.

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(Uploaded by Plazi for the Bat Literature Project) Habitat conversion creates a mosaic of land cover types, which affect the spatial distribution, diversity, and abundance of resources. We used abundance, functional, and phylogenetic information to determine if Neotropical bat communities exhibited phylogenetic or functional overdispersion or underdispersion in response to habitat conversion. Overdispersion suggests the operation of intraclade competition, niche partitioning, limiting similarity, or character displacement, whereas underdispersion indicates the operation of interclade competiti

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Presley, Steven J., Laura M. Cisneros, Christopher L. Higgins, Brian T. Klingbeil, Samuel M. Scheiner, and Michael R. Willig. "Phylogenetic and functional underdispersion in Neotropical phyllostomid bat communities." Biotropica 50, no. 1 (2018): 135–45. https://doi.org/10.5281/zenodo.13435179.

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(Uploaded by Plazi for the Bat Literature Project) Habitat conversion creates a mosaic of land cover types, which affect the spatial distribution, diversity, and abundance of resources. We used abundance, functional, and phylogenetic information to determine if Neotropical bat communities exhibited phylogenetic or functional overdispersion or underdispersion in response to habitat conversion. Overdispersion suggests the operation of intraclade competition, niche partitioning, limiting similarity, or character displacement, whereas underdispersion indicates the operation of interclade competiti

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26

Presley, Steven J., Laura M. Cisneros, Christopher L. Higgins, Brian T. Klingbeil, Samuel M. Scheiner, and Michael R. Willig. "Phylogenetic and functional underdispersion in Neotropical phyllostomid bat communities." Biotropica 50, no. 1 (2018): 135–45. https://doi.org/10.5281/zenodo.13435179.

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(Uploaded by Plazi for the Bat Literature Project) Habitat conversion creates a mosaic of land cover types, which affect the spatial distribution, diversity, and abundance of resources. We used abundance, functional, and phylogenetic information to determine if Neotropical bat communities exhibited phylogenetic or functional overdispersion or underdispersion in response to habitat conversion. Overdispersion suggests the operation of intraclade competition, niche partitioning, limiting similarity, or character displacement, whereas underdispersion indicates the operation of interclade competiti

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27

Presley, Steven J., Laura M. Cisneros, Christopher L. Higgins, Brian T. Klingbeil, Samuel M. Scheiner, and Michael R. Willig. "Phylogenetic and functional underdispersion in Neotropical phyllostomid bat communities." Biotropica 50, no. 1 (2018): 135–45. https://doi.org/10.5281/zenodo.13435179.

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(Uploaded by Plazi for the Bat Literature Project) Habitat conversion creates a mosaic of land cover types, which affect the spatial distribution, diversity, and abundance of resources. We used abundance, functional, and phylogenetic information to determine if Neotropical bat communities exhibited phylogenetic or functional overdispersion or underdispersion in response to habitat conversion. Overdispersion suggests the operation of intraclade competition, niche partitioning, limiting similarity, or character displacement, whereas underdispersion indicates the operation of interclade competiti

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28

Presley, Steven J., Laura M. Cisneros, Christopher L. Higgins, Brian T. Klingbeil, Samuel M. Scheiner, and Michael R. Willig. "Phylogenetic and functional underdispersion in Neotropical phyllostomid bat communities." Biotropica 50, no. 1 (2018): 135–45. https://doi.org/10.5281/zenodo.13435179.

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(Uploaded by Plazi for the Bat Literature Project) Habitat conversion creates a mosaic of land cover types, which affect the spatial distribution, diversity, and abundance of resources. We used abundance, functional, and phylogenetic information to determine if Neotropical bat communities exhibited phylogenetic or functional overdispersion or underdispersion in response to habitat conversion. Overdispersion suggests the operation of intraclade competition, niche partitioning, limiting similarity, or character displacement, whereas underdispersion indicates the operation of interclade competiti

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HARVEY, S. C., S. PATERSON, and M. E. VINEY. "Heterogeneity in the distribution of Strongyloides ratti infective stages among the faecal pellets of rats." Parasitology 119, no. 2 (1999): 227–35. http://dx.doi.org/10.1017/s0031182099004588.

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The distribution of helminth parasites within their host population is usually overdispersed and can be described by the negative binomial distribution. The causes of this overdispersion are poorly understood, but heterogeneity in the distribution of infective stages within the environment has been implicated as a possible factor. Here we describe the distribution of infective stages of the rat intestinal nematode parasite Strongyloides ratti among the faecal pellets of its host. The distribution of infective stages between faecal pellets is overdispersed and well described by the negative bin

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Sofro, A’yunin, Khusnia Nurul Khikmah, Danang Ariyanto, Yusuf Fuad, Budi Rahadjeng, and Yuliani Puji Astuti. "Handling Overdispersion Problems in Multinomial Logistic Regression (Study Case in Stress Level Data)." PROOF 3 (December 31, 2023): 78–83. http://dx.doi.org/10.37394/232020.2023.3.11.

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The development of statistical methods also impacts the development of analytical methods. One analytical method in which this is the case is the multinomial logistic regression modeling method. In this method, we have more than two categories of the response variable. At this time, the data used in modeling has various problems, one of which is overdispersion. This is a condition where there is a correlation between the response variables. This paper will examine the performance of multinomial logistic regression when there is overdispersion present in the data. We will focus on implementing

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Lester, R. J. G. "Overdispersion In Marine Fish Parasites." Journal of Parasitology 98, no. 4 (2012): 718–21. http://dx.doi.org/10.1645/ge-3017.1.

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Poortema, K. "On modelling overdispersion of counts." Statistica Neerlandica 53, no. 1 (1999): 5–20. http://dx.doi.org/10.1111/1467-9574.00094.

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Wong, Jackie S. T., Jonathan J. Forster, and Peter W. F. Smith. "Bayesian mortality forecasting with overdispersion." Insurance: Mathematics and Economics 83 (November 2018): 206–21. http://dx.doi.org/10.1016/j.insmatheco.2017.09.023.

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Jeong, Kwang Mo. "Modelling Count Responses with Overdispersion." Communications for Statistical Applications and Methods 19, no. 6 (2012): 761–70. http://dx.doi.org/10.5351/ckss.2012.19.6.761.

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Vedavathi, B., and B. Muniswamy. "Mixed Model Analysis for Overdispersion." International Journal of Engineering and Science 6, no. 05 (2017): 07–15. http://dx.doi.org/10.9790/1813-0605020715.

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Iannario, Maria. "Testing Overdispersion in CUBE Models." Communications in Statistics - Simulation and Computation 45, no. 5 (2014): 1621–35. http://dx.doi.org/10.1080/03610918.2014.936466.

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Bowman, K. O., and M. A. Kastenbaum. "Overdispersion of aggregated genetic data." Mutation Research/Environmental Mutagenesis and Related Subjects 272, no. 2 (1992): 133–37. http://dx.doi.org/10.1016/0165-1161(92)90041-j.

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Zhang, Tonglin, Zuoyi Zhang, and Ge Lin. "Spatial scan statistics with overdispersion." Statistics in Medicine 31, no. 8 (2011): 762–74. http://dx.doi.org/10.1002/sim.4404.

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Rao, R. Prabhakar, and B. C. Sutradhar. "A Global Test for the Goodness of Fit of Generalized Linear Models : An Estimating Equation Approach." Calcutta Statistical Association Bulletin 56, no. 1-4 (2005): 251–82. http://dx.doi.org/10.1177/0008068320050514.

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Summary Generalized linear models are used to analyze a wide variety of discrete and continuous data with possible overdispersion under the assumption that the data follow an exponential family of distributions. The violation of this assumption may have adverse effects on the statistical inferences. The existing goodness of fit tests for checking this assumption are valid only for a standard exponential family of distributions with no overdispersion. In this paper, we develop a global goodness of fit test for the general exponential family of distributions which may or may not contain overdisp

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Fávero, Luiz Paulo Lopes, Patrícia Belfiore, Marco Aurélio dos Santos, and R. Freitas Souza. "Overdisp: A Stata (and Mata) Package for Direct Detection of Overdispersion in Poisson and Negative Binomial Regression Models." Statistics, Optimization & Information Computing 8, no. 3 (2020): 773–89. http://dx.doi.org/10.19139/soic-2310-5070-557.

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Stata has several procedures that can be used in analyzing count-data regression models and, more specifically, in studying the behavior of the dependent variable, conditional on explanatory variables. Identifying overdispersion in countdata models is one of the most important procedures that allow researchers to correctly choose estimations such as Poisson or negative binomial, given the distribution of the dependent variable. The main purpose of this paper is to present a new command for the identification of overdispersion in the data as an alternative to the procedure presented by Cameron

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Letcher, Susan G. "Phylogenetic structure of angiosperm communities during tropical forest succession." Proceedings of the Royal Society B: Biological Sciences 277, no. 1678 (2009): 97–104. http://dx.doi.org/10.1098/rspb.2009.0865.

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The phylogenetic structure of ecological communities can shed light on assembly processes, but the focus of phylogenetic structure research thus far has been on mature ecosystems. Here, I present the first investigation of phylogenetic community structure during succession. In a replicated chronosequence of 30 sites in northeastern Costa Rica, I found strong phylogenetic overdispersion at multiple scales: species present at local sites were a non-random assemblage, more distantly related than chance would predict. Phylogenetic overdispersion was evident when comparing the species present at ea

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PRAMI MEITRIANI, DESAK PUTU, KOMANG GDE SUKARSA, and I. PUTU EKA NILA KENCANA. "PENERAPAN REGRESI QUASI-LIKELIHOOD PADA DATA CACAH (COUNT DATA) YANG MENGALAMI OVERDISPERSI DALAM REGRESI POISSON." E-Jurnal Matematika 2, no. 2 (2013): 37. http://dx.doi.org/10.24843/mtk.2013.v02.i02.p036.

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Poisson regression can be used to analyze count data, with assuming equidispersion. However, in the case of overdispersion often occur in the count data. The implementation of Poisson Regression can not be applied on this data because the data having overdispersion, that will lead to underestimate the standard error. Thus, use Quasi-Likelihood regression on this data. Quasi-Likelihood regression was also could not handle the overdispersion, but Quasi-Likelihood regression can improve the value of the standard error becomes greater than the value of the standard error on Poisson regression. Thu

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Ressa Nuryaningsih, Ani, and Nusar Hajarisman. "Perbandingan Model Regresi Zero Inflated Poisson (ZIP) dan Hurdle Poisson (HP) pada Kasus Kematian Balita di Kota Bandung Tahun 2021." Bandung Conference Series: Statistics 3, no. 2 (2023): 538–47. http://dx.doi.org/10.29313/bcss.v3i2.8522.

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Abstract. In this study, the response variable is assumed to be Poisson-distributed enumeration data. However, in the Poisson regression model, the enumerated data often deviates from the Poisson distribution because of the proportion of excess zero values in the response variable (excess zero), resulting in a larger variance than the average of the observed variables (overdispersion). Therefore, this study aims to model the data with Zero Inflated Poisson (ZIP) and Hurdle Poisson regression. Based on the results of the study by comparing the ZIP and Hurdle Poisson regression models using th

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Zhang, Hui, Hua He, Naiji Lu, et al. "A non-parametric model to address overdispersed count response in a longitudinal data setting with missingness." Statistical Methods in Medical Research 26, no. 3 (2015): 1461–75. http://dx.doi.org/10.1177/0962280215583397.

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Count responses are becoming increasingly important in biostatistical analysis because of the development of new biomedical techniques such as next-generation sequencing and digital polymerase chain reaction; a commonly met problem in modeling them with the popular Poisson model is overdispersion. Although it has been studied extensively for cross-sectional observations, addressing overdispersion for longitudinal data without parametric distributional assumptions remains challenging, especially with missing data. In this paper, we propose a method to detect overdispersion in repeated measures

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Fathurahman, M. "Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur." EKSPONENSIAL 13, no. 1 (2022): 79. http://dx.doi.org/10.30872/eksponensial.v13i1.888.

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Negative Binomial Regression (NBR) is an alternative regression model to model the relationship between the dependent variable in overdispersion count data and one or more independent variables. Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson regression model has a standard error value that is not under-estimated. The NBR model was applied to modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in

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Yuan, Yubai, Qi Xu, Agaz Wani, et al. "Differentially expressed heterogeneous overdispersion genes testing for count data." PLOS ONE 19, no. 7 (2024): e0300565. http://dx.doi.org/10.1371/journal.pone.0300565.

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The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to

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Taouali, Wahiba, Giacomo Benvenuti, Pascal Wallisch, Frédéric Chavane, and Laurent U. Perrinet. "Testing the odds of inherent vs. observed overdispersion in neural spike counts." Journal of Neurophysiology 115, no. 1 (2016): 434–44. http://dx.doi.org/10.1152/jn.00194.2015.

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The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electro

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Cepeda-Cuervo, Edilberto, and María Victoria Cifuentes-Amado. "Double Generalized Beta-Binomial and Negative Binomial Regression Models." Revista Colombiana de Estadística 40, no. 1 (2017): 141–63. http://dx.doi.org/10.15446/rce.v40n1.61779.

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Overdispersion is a common phenomenon in count datasets, that can greatly affect inferences about the model. In this paper develop three joint mean and dispersion regression models in order to fit overdispersed data. These models are based on reparameterizations of the beta-binomial and negative binomial distributions. Finally, we propose a Bayesian approach to estimate the parameters of the overdispersion regression models and use it to fit a school absenteeism dataset.

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Molina, E. A., T. M. F. Smith, and R. A. Sugden. "Modelling Overdispersion for Complex Survey Data." International Statistical Review / Revue Internationale de Statistique 69, no. 3 (2001): 373. http://dx.doi.org/10.2307/1403451.

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Pack, Simon E. "Hypothesis Testing for Proportions with Overdispersion." Biometrics 42, no. 4 (1986): 967. http://dx.doi.org/10.2307/2530712.

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