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

The Binomial Theorem01:30

The Binomial Theorem

341
The Binomial Theorem is a foundational principle in algebra used to expand expressions raised to a power. It provides a structured approach for expanding binomials of the form (a+b)n, where a and b are variables or constants representing algebraic expressions, and n is a non-negative integer.The general form of the Binomial Theorem is:Each term in the expansion involves a binomial coefficient, which is calculated using factorials:The exponent of a in each term decreases from n to 0, while the...
341
Binomial Probability Distribution01:15

Binomial Probability Distribution

16.0K
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,...
16.0K
Binomial Expansion Using Pascal's Triangle01:30

Binomial Expansion Using Pascal's Triangle

270
Expanding a binomial expression such as (a + b)n results in a predictable sequence of terms that can be systematically derived using Pascal’s Triangle. This triangular array of numbers plays a central role in understanding and computing the coefficients of binomial expansions.Pascal’s Triangle is constructed such that each row corresponds to the coefficients of a binomial raised to a power. The topmost row, known as the zeroth row, corresponds to (a + b)0, and each successive row...
270
Negative Regulator Molecules01:23

Negative Regulator Molecules

38.6K
Positive regulators allow a cell to advance through cell cycle checkpoints. Negative regulators have an equally important role as they terminate a cell’s progression through the cell cycle—or pause it—until the cell meets specific criteria.
38.6K
Positive, Negative, and Zero Work00:58

Positive, Negative, and Zero Work

22.3K
Work is done on an object when energy is transferred to the object. In other words, work is done when a force acts on a body that undergoes a displacement from one position to another. By definition, the work done by a force is the integral of the force with respect to the displacement along its path. Forces can vary as a function of position, and displacements can occur along various paths between two points. The magnitude of a force multiplied by the cosine of the angle that the force makes...
22.3K
Gravitational Potential Energy for Extended Objects01:07

Gravitational Potential Energy for Extended Objects

2.0K
Consider a system comprising several point masses. The coordinates of the center of mass for this system can be expressed as the summation of the product of each mass and its position vector divided by the total mass:
2.0K

You might also read

Related Articles

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

Sort by
Same authorSame journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same author

Nationwide seroprevalence of severe fever with thrombocytopenia syndrome virus in South Korea: Regional patterns and public health implications.

Ticks and tick-borne diseases·2026
Same author

Overcoming Dilution of Collision Probability in Satellite Conjunction Analysis via Confidence Distribution.

Entropy (Basel, Switzerland)·2025
Same author

Seroprevalence and Epidemiological Insights into Severe Fever with Thrombocytopenia Syndrome on Jeju Island, Republic of Korea.

Viruses·2025
Same author

Development of a Risk Tracking Model for Neurological Deterioration in Ischemic Stroke Based on Blood Pressure Dynamics.

Journal of the American Heart Association·2025
Same author

Semiparametric estimation for nonparametric frailty models using nonparametric maximum likelihood approach.

Statistical methods in medical research·2021
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Feb 12, 2026

Author Spotlight: Evaluating Therapeutic Strategies to Enhance Liver Regeneration
05:25

Author Spotlight: Evaluating Therapeutic Strategies to Enhance Liver Regeneration

Published on: May 24, 2024

3.6K

Extended negative binomial hurdle models.

Maengseok Noh1, Youngjo Lee2

  • 11 Department of Statistics, Pukyong National University, Busan, Republic of Korea.

Statistical Methods in Medical Research
|April 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new random-effect model to handle count data with excessive zeros and underdispersion, addressing limitations of existing Poisson models for complex real-world data.

Keywords:
Excessive zerosPoisson hurdle modelhierarchical generalized linear modelunderdispersionzero-inflated Poisson model

More Related Videos

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
08:46

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

Published on: August 12, 2020

7.0K
Porcine Liver Transplantation Without Veno-Venous Bypass As an Extended Criteria Donor Model
12:49

Porcine Liver Transplantation Without Veno-Venous Bypass As an Extended Criteria Donor Model

Published on: August 17, 2022

3.1K

Related Experiment Videos

Last Updated: Feb 12, 2026

Author Spotlight: Evaluating Therapeutic Strategies to Enhance Liver Regeneration
05:25

Author Spotlight: Evaluating Therapeutic Strategies to Enhance Liver Regeneration

Published on: May 24, 2024

3.6K
A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
08:46

A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis

Published on: August 12, 2020

7.0K
Porcine Liver Transplantation Without Veno-Venous Bypass As an Extended Criteria Donor Model
12:49

Porcine Liver Transplantation Without Veno-Venous Bypass As an Extended Criteria Donor Model

Published on: August 17, 2022

3.1K

Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Poisson models are standard for count data but struggle with zero-inflation/deflation and over/underdispersion.
  • Existing models lack the flexibility to address excessive zeros coupled with underdispersion in non-zero counts.
  • There is a documented need for statistical models accommodating these specific data characteristics.

Purpose of the Study:

  • To develop a novel statistical model for count data exhibiting excessive zeros and underdispersion.
  • To provide a model capable of handling higher degrees of overdispersion when zero rates are high.
  • To offer a robust solution for real-world data analyses with complex zero patterns and dispersion issues.

Main Methods:

  • Utilized a random-effect model framework.
  • Developed a general statistical model to accommodate specific count data phenomena.
  • Focused on addressing simultaneous excessive zeros and underdispersion in non-zero counts.

Main Results:

  • The proposed random-effect model successfully accommodates excessive zeros.
  • The model allows for underdispersion in non-zero counts.
  • It provides a more flexible approach to overdispersion compared to existing models.

Conclusions:

  • The developed random-effect model offers a significant advancement for count data analysis.
  • It effectively addresses the limitations of traditional Poisson models in specific scenarios.
  • This model provides a valuable tool for researchers dealing with complex count data distributions.