Search engine for discovering works of Art, research articles, and books related to Art and Culture
Javascript must be enabled to continue!
Overdispersion
View through CrossRef
AbstractCount data analyzed under a Poisson assumption or data in the form of proportions analyzed under a binomial assumption often exhibit overdispersion, where the empirical variance in the data is greater than that predicted by the model. This entry illustrates how overdispersion may arise and discusses the consequences of ignoring it, in particular, the underestimation of standard errors of covariate effects. Straightforward methods for ascertaining whether overdispersion is evident are presented. Simple methods for incorporating overdispersion within the framework of quasi‐likelihood estimation are reviewed as well as generalized linear mixed models, which form a broad scheme for model‐based analysis when overdispersion is present.
Title: Overdispersion
Description:
AbstractCount data analyzed under a Poisson assumption or data in the form of proportions analyzed under a binomial assumption often exhibit overdispersion, where the empirical variance in the data is greater than that predicted by the model.
This entry illustrates how overdispersion may arise and discusses the consequences of ignoring it, in particular, the underestimation of standard errors of covariate effects.
Straightforward methods for ascertaining whether overdispersion is evident are presented.
Simple methods for incorporating overdispersion within the framework of quasi‐likelihood estimation are reviewed as well as generalized linear mixed models, which form a broad scheme for model‐based analysis when overdispersion is present.
Related Results
A population genetics model explaining overdispersion in active transposable elements
A population genetics model explaining overdispersion in active transposable elements
Abstract
The number of transposable elements (TEs) per host genome varies within natural populations, with variance much greater than the mean. T...
Dealing with overdispersed count data in applied ecology
Dealing with overdispersed count data in applied ecology
Summary
The ability to identify key ecological processes is important when solving applied problems. Increasingly, ecologists are adopting Akaike's information criterion (AIC) as ...
Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur
Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur
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 independ...
Phylogenetic overdispersion of plant species in southern Brazilian savannas
Phylogenetic overdispersion of plant species in southern Brazilian savannas
Ecological communities are the result of not only present ecological processes, such as competition among species and environmental filtering, but also past and continuing evolutio...
Overdispersion models for correlated multinomial data: Applications to blinding assessment
Overdispersion models for correlated multinomial data: Applications to blinding assessment
Overdispersion models have been extensively studied for correlated normal and binomial data but much less so for correlated multinomial data. In this work, we describe a multinomia...
Pemodelan Distribusi Poisson-Sujatha pada Data Frekuensi Klaim Asuransi Kendaraan Bermotor di Indonesia
Pemodelan Distribusi Poisson-Sujatha pada Data Frekuensi Klaim Asuransi Kendaraan Bermotor di Indonesia
Abstract. Insurance is an agreement between two or more parties where the insurer promises to the insured by receiving a premium to compensate the insured against loss, damage or l...
Pemodelan dan Pemetaan Jumlah Penderita Kusta di Jawa Barat dengan Regresi Binomial Negatif dan Flexibly Shaped Spatial Scan Statistic
Pemodelan dan Pemetaan Jumlah Penderita Kusta di Jawa Barat dengan Regresi Binomial Negatif dan Flexibly Shaped Spatial Scan Statistic
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 f...
Generalized Tree-Based Machine Learning Methods with Applications to Small Area Estimation
Generalized Tree-Based Machine Learning Methods with Applications to Small Area Estimation
Chapter 1 - Identifying and addressing poverty is challenging in administrative units with limited information on income distribution and well-being. To overcome this obstacle, sma...