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's Ratio01:23

Poisson's Ratio

1.2K
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...
1.2K
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.1K
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...
12.1K
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

4.3K
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.
4.3K
Margin of Error01:27

Margin of Error

7.7K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.7K
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

636
Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
636
Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

607
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
607

You might also read

Related Articles

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

Sort by
Same author

Substrate and target selectivity of 4'-fluoroadenosine against viral and host polymerases.

bioRxiv : the preprint server for biology·2026
Same author

The Q226H Mutation in Avian H5N1 Hemagglutinin Mediates a Path towards Structural Adaptation in Humans.

bioRxiv : the preprint server for biology·2026
Same author

Preferential remdesivir triphosphate incorporation by SARS-CoV-2 polymerase is altered to ATP by the S759A mutation.

Communications biology·2026
Same author

Progeny effects of rotenone exposure depend on parental toxicity.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same author

Protocol to investigate human mitochondrial transcription initiation by integrating biochemical and cryo-EM approaches.

STAR protocols·2026
Same author

Inhibitory effects of molnupiravir on Crimean-Congo hemorrhagic fever virus polymerase.

NAR molecular medicine·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to Set4 <math><semantics><mi>Δ</mi> <annotation>$\Delta$</annotation></semantics></math> Mutant Data.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.4K

Marginalized zero-inflated Poisson models with missing covariates.

Habtamu K Benecha1, John S Preisser2, Kimon Divaris3,4

  • 1National Agricultural Statistics Service, USDA, Washington, USA.

Biometrical Journal. Biometrische Zeitschrift
|May 12, 2018
PubMed
Summary
This summary is machine-generated.

Marginalized zero-inflated Poisson (MZIP) models offer interpretable results for count data. This study introduces a new method to handle missing covariate data in MZIP models, improving analysis accuracy.

Keywords:
Monte Carlo EMmarginalized modelsmissing at randommissing datazero-inflation

More Related Videos

Perfusion and Inflation of the Mouse Lung for Tumor Histology
04:24

Perfusion and Inflation of the Mouse Lung for Tumor Histology

Published on: August 6, 2020

21.5K
Air-Inflation of Murine Lungs with Vascular Perfusion-Fixation
07:19

Air-Inflation of Murine Lungs with Vascular Perfusion-Fixation

Published on: February 2, 2021

8.5K

Related Experiment Videos

Last Updated: Feb 10, 2026

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.4K
Perfusion and Inflation of the Mouse Lung for Tumor Histology
04:24

Perfusion and Inflation of the Mouse Lung for Tumor Histology

Published on: August 6, 2020

21.5K
Air-Inflation of Murine Lungs with Vascular Perfusion-Fixation
07:19

Air-Inflation of Murine Lungs with Vascular Perfusion-Fixation

Published on: February 2, 2021

8.5K

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Marginalized zero-inflated Poisson (MZIP) models provide interpretable estimates for count data with excess zeros.
  • Complete case analysis is often used for missing covariate data in count models due to a lack of suitable statistical methods and software.
  • This limitation hinders accurate analysis in various research fields.

Purpose of the Study:

  • To present a novel estimation method for MZIP models when dealing with missing covariate data.
  • To demonstrate the applicability of this method to other missing data scenarios.
  • To compare the performance of the new method against complete case analysis.

Main Methods:

  • Development of a new statistical estimation method for MZIP models accommodating missing covariates.
  • Application of the method to simulated datasets.
  • Validation using real-world dental data from a school-based fluoride mouthrinse program.

Main Results:

  • The proposed estimation method effectively handles missing covariates in MZIP models.
  • Simulations and dental data analysis showed the new method provides more accurate and reliable estimates compared to complete case analysis.
  • The method offers a viable alternative for analyzing count data with missing covariate information.

Conclusions:

  • The presented method offers a robust solution for MZIP model estimation in the presence of missing covariates.
  • This advancement facilitates more accurate statistical inference in epidemiological and biostatistical studies.
  • The approach is generalizable to other statistical models with missing data challenges.