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

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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

You might also read

Related Articles

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

Sort by
Same author

A novel maternal prenatal risk index to predict mortality-weighted severe maternal morbidity at hospitalization: a retrospective cohort study.

Lancet regional health. Americas·2026
Same author

Information-Based Composite Likelihood Method for Hybrid Meta-Analysis Integrating Individual Participant Data and Aggregated Data.

Statistics in medicine·2026
Same author

Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.

Statistics in biosciences·2026
Same author

Mortality-weighted severe maternal morbidity: a novel approach to assessing maternal health outcomes.

BMC pregnancy and childbirth·2025
Same author

Pair-Feeding Study Designs Can Create Biases and Inflate Type I Error Rates: A Simulation Study.

Obesity (Silver Spring, Md.)·2025
Same author

Bayesian network meta-regression for aggregate ordinal outcomes with imprecise categories.

Journal of biopharmaceutical statistics·2025
Same journal

A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.

Statistica Sinica·2026
Same journal

DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA.

Statistica Sinica·2026
Same journal

Longitudinal Modeling of Rank-based Global Outcome.

Statistica Sinica·2026
Same journal

INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS.

Statistica Sinica·2026
Same journal

COMMUNITY EXTRACTION OF NETWORK DATA UNDER STOCHASTIC BLOCK MODELS.

Statistica Sinica·2026
Same journal

STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.

Statistica Sinica·2025
See all related articles

Related Experiment Video

Updated: Jun 10, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

An Information Matrix Prior for Bayesian Analysis in Generalized Linear Models with High Dimensional Data.

Mayetri Gupta1, Joseph G Ibrahim

  • 1Department of Biostatistics, Boston University, MA 02118, U.S.A. Email: gupta@bu.edu ; ;

Statistica Sinica
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces Information Matrix (IM) priors for high-dimensional generalized linear models, extending them to the "p > n" problem with Information Matrix Ridge (IMR) priors. These novel priors offer advantages over existing methods in high-dimensional data analysis.

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Jun 10, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Statistics
  • Machine Learning
  • Bioinformatics

Background:

  • High-dimensional data analysis presents challenges, particularly the

Purpose of the Study:

  • Develop novel Information Matrix (IM) priors for high-dimensional generalized linear models.
  • Extend IM priors to address the

Main Methods:

  • Constructed Information Matrix (IM) priors based on a generalization of Zellner's g-prior.
  • Developed Information Matrix Ridge (IMR) priors by incorporating a ridge parameter for the "p > n" scenario.
  • Derived theoretical properties of the priors and posteriors, including moment generating functions and tail behavior.

Main Results:

  • Demonstrated the existence and properties of IM and IMR priors and their implied posteriors.
  • Showcased advantages of IM and IMR priors over Gaussian and g-priors in high-dimensional settings through simulations.
  • Applied the novel priors to a real-world nucleosomal positioning data set.

Conclusions:

  • Information Matrix (IM) and Information Matrix Ridge (IMR) priors provide a robust framework for high-dimensional regression.
  • The proposed priors offer superior performance compared to existing methods in "p > n" scenarios.
  • The methodology is effective for analyzing complex biological data, such as nucleosomal positioning.