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

667
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...
667
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

1.8K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.8K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

206
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
206
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

665
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
665
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

589
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...
589
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.0K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Predictors of Medical Debt Sent to Collections after Radiotherapy.

International journal of radiation oncology, biology, physics·2026
Same author

Prognostic Impact of Brain Radiotherapy and Lactate Dehydrogenase in Melanoma with Brain Metastases: A Retrospective Cohort Study.

Cancers·2026
Same author

Impact of Smoking on Treatment Outcomes in Patients With Human Papillomavirus-Related Oropharyngeal Cancer.

Head & neck·2026
Same author

Correction: The FGF-FGFR axis as an immune-metabolic rheostat in gastrointestinal inflammation and cancer.

Frontiers in immunology·2026
Same author

Comparative Performance of Prostate Cancer Risk Prediction Tools in Gay, Bisexual, and Other Men Who Have Sex With Men vs Heterosexual Men.

Urology·2026
Same author

The FGF-FGFR axis as an immune-metabolic rheostat in gastrointestinal inflammation and cancer.

Frontiers in immunology·2026
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Sep 8, 2025

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

2.2K

Testing for correlation between two time series using a parametric bootstrap.

Zequn Sun1, Thomas J Fisher2

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for detecting correlations in the variance of time series data. Parametric bootstrapping proves effective for improving accuracy in real-world financial analyses.

Keywords:
62F4062M10CausalityGARCHcross-correlationportmanteautime series

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K
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

Related Experiment Videos

Last Updated: Sep 8, 2025

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

2.2K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K
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

Area of Science:

  • Statistics
  • Econometrics
  • Time Series Analysis

Background:

  • Traditional methods for time series analysis often focus on mean correlation.
  • Assessing variance correlation is crucial for understanding complex financial dynamics.
  • Existing tests may lack efficacy in finite sample scenarios.

Purpose of the Study:

  • To adapt existing statistical tests for assessing cross-correlation in time series variance.
  • To evaluate the performance of these adapted tests, particularly in finite samples.
  • To introduce and validate a parametric bootstrapping approach for improved accuracy.

Main Methods:

  • Exploration and formal introduction of test statistics for variance cross-correlation.
  • Monte Carlo simulations to assess theoretical asymptotic distributions.
  • Application and validation of parametric bootstrapping.
  • Empirical analysis using financial market data.

Main Results:

  • Theoretical asymptotic distributions can be unreliable in finite samples.
  • Parametric bootstrapping significantly enhances the accuracy of variance correlation detection.
  • The proposed method demonstrates robustness in analyzing financial time series.

Conclusions:

  • Parametric bootstrapping is a viable and effective method for time series variance correlation.
  • The developed techniques offer a robust approach for financial market analysis.
  • This research provides valuable tools for understanding interdependencies in financial data.