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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

369
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
369
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

611
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
611
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

1.1K
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
1.1K
Potential-Energy Criterion for Equilibrium01:16

Potential-Energy Criterion for Equilibrium

952
Potential energy or potential function plays an essential role in determining the stability of a mechanical system. If a system is subjected to both gravitational and elastic forces, the potential function of the system can be expressed as the algebraic sum of gravitational and elastic potential energy. If the system is in equilibrium and is displaced by a small amount, then the work done on the system equals the negative of the change in the system's potential energy from the initial to the...
952
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
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.8K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.8K

You might also read

Related Articles

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

Sort by
Same author

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same author

Addressing outliers in mixed-effects logistic regression: a more robust modeling approach.

Journal of applied statistics·2026
Same author

Focused information criteria for model selection - a Bayesian perspective.

Journal of applied statistics·2026
Same author

Bayesian generalized method of moments applied to pseudo-observations in survival analysis.

Lifetime data analysis·2025
Same author

The Evidence-Based Medicine Management of Endometriosis Should Be Updated for the Limitations of Trial Evidence, the Multivariability of Decisions, Collective Experience, Heuristics, and Bayesian Thinking.

Journal of clinical medicine·2025
Same author

Joint quantile regression of longitudinal continuous proportions and time-to-event data: Application in medication adherence and persistence.

Statistical methods in medical research·2024

Related Experiment Video

Updated: Feb 12, 2026

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

7.3K

Comparing hierarchical models via the marginalized deviance information criterion.

Adrian Quintero1, Emmanuel Lesaffre1

  • 1IBioStat, KU Leuven University of Leuven, Leuven, Belgium.

Statistics in Medicine
|March 27, 2018
PubMed
Summary
This summary is machine-generated.

We present a new method to compute the marginalized deviance information criterion (mDIC) for hierarchical models. This approach, based on generating replicate samples, overcomes computational challenges and offers a more reliable alternative to conditional DIC (cDIC) in Bayesian analysis.

Keywords:
Markov chain Monte Carlo methodslatent variableobserved informationreplication method

More Related Videos

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold
05:28

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Published on: February 10, 2023

2.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.8K

Related Experiment Videos

Last Updated: Feb 12, 2026

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

7.3K
Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold
05:28

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Published on: February 10, 2023

2.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.8K

Area of Science:

  • Statistics
  • Computational Biology
  • Biostatistics

Background:

  • Hierarchical models are crucial in pharmacokinetics and longitudinal studies.
  • Bayesian estimation often uses the deviance information criterion (DIC) for model comparison.
  • Existing DIC versions, conditional DIC (cDIC) and marginalized DIC (mDIC), have limitations.

Purpose of the Study:

  • To introduce a computationally feasible method for calculating mDIC in hierarchical models.
  • To address the practical limitations of existing DIC methods, particularly cDIC.
  • To provide a more accurate model comparison tool for complex Bayesian analyses.

Main Methods:

  • Developed a method to compute mDIC by generating replicate samples of latent variables.
  • Utilized Markov chain Monte Carlo (MCMC) output from Bayesian packages.
  • Proposed approximations to reduce computational complexity for large datasets.

Main Results:

  • The proposed method for mDIC calculation is broadly applicable to hierarchical models.
  • Demonstrated the method's efficacy using simulated data and two medical studies.
  • Showcased that cDIC can be misleading, while mDIC provides pertinent model comparison.

Conclusions:

  • The novel mDIC computation method offers a practical and accurate solution for Bayesian hierarchical model comparison.
  • This approach enhances the reliability of model selection in pharmacokinetics and longitudinal studies.
  • Researchers can now more confidently utilize mDIC, avoiding potential pitfalls of cDIC.