Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Poisson's Ratio01:23

Poisson's Ratio

Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign ensures...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Missing data in ecology: Syntheses, clarifications, and considerations.

Ecological monographs·2026
Same author

Ringed Seal (<i>Pusa hispida</i>) Haul-Out Behavior and Emergence Timing in the Bering, Chukchi, and Beaufort Seas.

Ecology and evolution·2026
Same author

Correction: SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks.

PloS one·2026
Same author

An integrated data model to estimate abundance from counts with temporal dependence and imperfect detection.

Ecology·2025
Same author

Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data.

Spatial statistics·2025
Same author

Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation.

Environmetrics·2024

Related Experiment Video

Updated: Jul 9, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?

Jay M Ver Hoef1, Peter L Boveng

  • 1National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE, Building 4, Seattle, Washington 98115-6349, USA. jay.verhoef@noaa.gov

Ecology
|December 7, 2007
PubMed
Summary

Quasi-Poisson and negative binomial regression models can yield different results for overdispersed count data due to how they weight data. Understanding these differences helps ecologists choose the appropriate regression method.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jul 9, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Ecology
  • Statistical Modeling

Background:

  • Overdispersed count data are common in ecological studies.
  • Quasi-Poisson and negative binomial regression are often used for such data.
  • These models have similar parameterization but differ in variance assumptions.

Purpose of the Study:

  • To explain the reasons for discrepancies between quasi-Poisson and negative binomial regression models.
  • To illustrate how these differences impact covariate effect estimation.
  • To guide ecologists in selecting the most suitable regression model.

Main Methods:

  • Comparison of quasi-Poisson (linear mean-variance) and negative binomial (quadratic mean-variance) models.
  • Analysis of how variance-mean relationships influence iteratively weighted least-squares fitting.
  • Application to harbor seal count data from aerial surveys.

Main Results:

  • Striking differences in covariate effect estimation can occur between the two models.
  • The models assign different weights to small and large counts based on their variance functions.
  • Harbor seal abundance estimates varied dramatically when using quasi-Poisson versus negative binomial regression.

Conclusions:

  • The choice between quasi-Poisson and negative binomial regression depends on their distinct weighting schemes.
  • Understanding these weighting differences is crucial for accurate ecological data analysis.
  • This knowledge aids ecologists in selecting appropriate statistical models for count data.