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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

562
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...
562
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

83
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
83
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

120
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...
120
Poisson Probability Distribution01:09

Poisson Probability Distribution

8.4K
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...
8.4K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

117
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
117

You might also read

Related Articles

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

Sort by
Same author

Delaying bud-break on pecan trees: a Bayesian longitudinal multinomial regression approach.

Journal of applied statistics·2025
Same author

A Bayesian Genomic Regression Model with Skew Normal Random Errors.

G3 (Bethesda, Md.)·2018
See all related articles

Related Experiment Video

Updated: Aug 30, 2025

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

10.8K

Modeling nematode population dynamics using a multivariate poisson model with spike and slab variable selection.

Gill Giese1, Dayna P Saldaña Zepeda2, Jacquelin Beacham3

  • 1Agricultural Science Center at Los Lunas, NMSU, Los Lunas, NM, USA.

Journal of Applied Statistics
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to understand nematode population dynamics in soil. The model reveals interactions between nematode genera, aiding soil health management strategies.

Keywords:
NUTS algorithmNematode correlationmultivariate Poisson lognormalorganism–habitat relationshipparsimonious models

More Related Videos

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

3.4K
Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans
07:04

Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans

Published on: August 15, 2020

5.5K

Related Experiment Videos

Last Updated: Aug 30, 2025

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

10.8K
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

3.4K
Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans
07:04

Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans

Published on: August 15, 2020

5.5K

Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Modeling

Background:

  • Modeling organism dynamics, especially count correlated data, presents significant challenges in ecological research.
  • Understanding nematode population dynamics is crucial for soil health assessment and management.

Purpose of the Study:

  • To adapt the multivariate Poisson distribution for modeling correlated nematode count data.
  • To identify key environmental covariates influencing nematode occurrence and interactions.
  • To develop a parsimonious statistical model for nematode dynamics.

Main Methods:

  • Utilized a multivariate Poisson distribution to model correlated nematode genera counts.
  • Applied Spike and Slab Variable Selection for parsimonious model development.
  • Analyzed a dataset comprising 68 soil samples, 11 nematode genera, and 12 soil parameters.
  • Assessed genus-to-genus interactions via a model-derived correlation matrix.

Main Results:

  • Identified significant covariates affecting individual nematode genera.
  • Classified nematode pairs as sympathetic, antagonistic, or neutral based on estimated correlations.
  • Validated the model's performance through a simulation study.
  • Recovered correlations among nematode genera, relaxing the mean-equal-variance constraint of univariate Poisson models.

Conclusions:

  • The developed model effectively captures complex nematode population dynamics and inter-genus correlations.
  • Findings provide valuable insights for soil health management and ecological research.
  • The model offers a robust tool for researchers and practitioners studying soil ecosystems.