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

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
Bootstrapping01:24

Bootstrapping

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 small or...
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...

You might also read

Related Articles

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

Sort by
Same author

Investigation on the Cytotoxicity of Hydroxycinnamic and Hydroxybenzoic Acid-Based Natural Antioxidant Conjugated Terpyridine Analogues toward Triple-Negative Breast Cancer.

ACS medicinal chemistry letters·2026
Same author

Liver Injury From Medications Used for Treating Inflammatory Bowel Disease: The Drug-Induced Liver Injury Network Experience.

Liver international : official journal of the International Association for the Study of the Liver·2026
Same author

Co-expression of superoxide and peroxide scavenging antioxidant enzymes rescues β cells from hypoxia and hyperglycemia-induced oxidative damage.

Tissue & cell·2026
Same author

DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS.

The annals of applied statistics·2025
Same author

Role of microalgae in reducing antibiotic-resistant bacteria in synthetic wastewater.

Environmental monitoring and assessment·2025
Same author

DRESS Syndrome in Patients With Drug-Induced Liver Injury: Characteristics and HLA Risk Factors.

The American journal of gastroenterology·2025
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Bayesian bootstrap estimation of ROC curve.

Jiezhun Gu1, Subhashis Ghosal, Anindya Roy

  • 1Duke Clinical Research Institute, Duke University Medical Center, Durham, NC 27715, USA. jiezhun.gu@duke.edu

Statistics in Medicine
|July 10, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces the Bayesian bootstrap (BB) method for estimating Receiver Operating Characteristic (ROC) curves and their functionals, like the Area Under the Curve (AUC). The BB method offers an accurate, robust, and simple nonparametric approach for diagnostic and prognostic test analysis.

More Related Videos

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Related Experiment Videos

Last Updated: Jul 3, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Area of Science:

  • Medical Statistics
  • Biostatistics
  • Diagnostic Test Evaluation

Background:

  • Receiver Operating Characteristic (ROC) curve analysis is crucial for evaluating diagnostic and prognostic tests.
  • Existing parametric and semiparametric methods for ROC curve estimation have limitations.
  • There is a need for robust and accurate nonparametric estimation methods.

Purpose of the Study:

  • To propose the Bayesian bootstrap (BB) as a fully nonparametric method for ROC curve and functional estimation.
  • To evaluate the performance of the BB method compared to existing techniques.
  • To introduce a BB-based procedure for testing the binormality assumption.

Main Methods:

  • Implementation of the Bayesian bootstrap (BB) for nonparametric ROC curve estimation.
  • Utilizing integrated absolute error to assess the accuracy of ROC curve estimates in simulations.
  • Developing a BB-based procedure for binormality assumption testing.

Main Results:

  • The BB method provides a bandwidth-free smoothing approach to empirical ROC estimates.
  • Simulation studies demonstrate the BB method's accuracy, robustness, and simplicity.
  • The BB method performs favorably compared to other existing ROC curve estimation techniques.

Conclusions:

  • The Bayesian bootstrap is a valuable nonparametric tool for ROC analysis.
  • The BB method offers a reliable alternative for estimating ROC curves and functionals.
  • The proposed BB procedure aids in validating the binormality assumption in ROC analysis.