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

Bootstrapping01:24

Bootstrapping

602
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
602
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

476
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...
476
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

131
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
131
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
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...
68
Survival Tree01:19

Survival Tree

79
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
79

You might also read

Related Articles

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

Sort by
Same author

Inflammasomes in digestive diseases: mechanisms and therapeutic potential.

Molecular biology reports·2026
Same author

Corrigendum to "Effects of abamectin sublethal doses on the invasive pest Tuta absoluta: Integration of population parameters and transcriptome analysis" [Pesticide Biochemistry and Physiology 218 (2026) 106924].

Pesticide biochemistry and physiology·2026
Same author

Simultaneous inhibition of Microcystis growth, toxin production and release by a high-efficacy algicidal bacterium Aeromonas sp. N3.

Journal of applied microbiology·2026
Same author

SARS-CoV-2 ORF3a suppresses host antiviral interferon responses by promoting STUB1-mediated PTEN proteasomal degradation.

Journal of virology·2026
Same author

Extracting Genetically-Imputed Causal Features From ECG Data.

Statistical analysis and data mining·2026
Same author

Zn-dihydromyricetin/Cu-dual-network hydrogel antimicrobial coating for cascade repair of bone defects.

Biomaterials advances·2026
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 24, 2025

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

3.3K

LARGE MULTIPLE GRAPHICAL MODEL INFERENCE VIA BOOTSTRAP.

Yongli Zhang1, Xiaotong Shen1, Shaoli Wang1

  • 1University of Oregon, University of Minnesota, Minneapolis and Shanghai University of Finance and Economics.

Statistica Sinica
|June 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a bootstrap method to detect structural changes in large economic networks. The new approach accurately infers financial network shifts, even in high-dimensional settings, outperforming existing methods.

Keywords:
Bootstrapgraphical modelshigh-dimensional inferencemodel selectionregularization

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

Related Experiment Videos

Last Updated: Jun 24, 2025

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

3.3K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

Area of Science:

  • Econometrics
  • Network Analysis
  • Statistical Inference

Background:

  • Economic and financial networks are susceptible to structural changes from external shocks.
  • Detecting these changes is crucial for understanding network dynamics.
  • High-dimensional models present challenges due to bias and uncertainty in regularization.

Purpose of the Study:

  • To develop a robust method for detecting structural changes in large, high-dimensional economic and financial networks.
  • To address the challenges of bias and uncertainty in overparameterized models.
  • To provide a statistically sound inference framework for network analysis.

Main Methods:

  • Utilized multiple Gaussian Graphical Models to represent network structures.
  • Employed the bootstrap method to approximate the sampling distribution of a likelihood ratio test statistic.
  • Developed a theoretical framework for asymptotic inference in high-dimensional settings.

Main Results:

  • The proposed bootstrap method ensures correct asymptotic inference, irrespective of the test statistic's distribution.
  • Simulations demonstrated superior performance compared to the Likelihood Ratio Test.
  • Analysis of a stock network revealed increased connectivity post-2007-2009 financial crisis.

Conclusions:

  • The bootstrap method offers a reliable approach for structural change detection in financial networks.
  • Financial crises significantly alter network connectivity, with varying responses among individual assets.
  • The findings provide insights into network resilience and adaptation mechanisms.