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

Kruskal-Wallis Test01:19

Kruskal-Wallis Test

822
The Kruskal-Wallis test, also known as the Kruskal-Wallis H test, serves as a nonparametric alternative to the one-way ANOVA, offering a solution for analyzing the differences across three or more independent groups based on a single, ordinal-dependent variable. This statistical test is particularly valuable in scenarios where the data does not meet the normal distribution assumption required by its parametric counterparts. Kruskal-Wallis test is designed typically to handle ordinal data or...
822
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

250
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...
250
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

139
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
139
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

158
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,...
158
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

444
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...
444

You might also read

Related Articles

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

Sort by
Same journal

Opinion Dynamic and Social Clustering in a 2D Space: An Agent Based Experiment.

Computational economics·2026
Same journal

Competitive Pricing Using Model-Based Bandits.

Computational economics·2025
Same journal

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.

Computational economics·2024
Same journal

On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach.

Computational economics·2023
Same journal

Stocks Opening Price Gaps and Adjustments to New Information.

Computational economics·2023
Same journal

The Rise and Fall of Financial Flows in EU 15: New Evidence Using Dynamic Panels with Common Correlated Effects.

Computational economics·2023
See all related articles

Related Experiment Video

Updated: Jul 25, 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

3.4K

Nonparametric Test for Volatility in Clustered Multiple Time Series.

Erniel B Barrios1, Paolo Victor T Redondo2

  • 1Monash University Malaysia, Selangor, Malaysia.

Computational Economics
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new bootstrap test for multiple time series to address volatility clustering and contagion effects in financial markets. This method improves statistical power and accuracy, especially for stationary time series data.

Keywords:
ClusteringMultiple time seriesNonparametric testSieve bootstrapVolatility

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Related Experiment Videos

Last Updated: Jul 25, 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

3.4K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Area of Science:

  • Econometrics
  • Financial Time Series Analysis
  • Statistical Modeling

Background:

  • Volatility clustering in multiple time series, such as stock market indicators, complicates volatility analysis.
  • Parametric tests may exhibit issues with size and power due to contagion effects.
  • Existing methods may not adequately address the complexities of interdependent financial time series.

Purpose of the Study:

  • To propose a novel statistical test for volatility in multiple time series.
  • To account for the potential presence of contagion effects in financial data.
  • To provide a robust method for analyzing volatility that is less dependent on distributional assumptions.

Main Methods:

  • Development of a volatility test utilizing the bootstrap method for multiple time series.
  • Application of the test to data exhibiting potential contagion effects.
  • Evaluation of the test's performance under various time series properties, including nonstationarity.

Main Results:

  • The proposed bootstrap test is robust to distributional assumptions.
  • The test demonstrates correct sizing even with almost nonstationary time series.
  • The test shows significant power, particularly when time series are stationary in mean and volatility clusters are few.

Conclusions:

  • The bootstrap-based volatility test effectively handles contagion effects in multiple time series.
  • This method offers improved accuracy and power compared to traditional parametric tests.
  • The approach is validated using global stock price data, demonstrating practical applicability.