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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
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...
1.5K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

193
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
193
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.7K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.7K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

615
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
615
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K

You might also read

Related Articles

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

Sort by
Same author

KERNEL-SMOOTHED CONDITIONAL QUANTILES OF CORRELATED BIVARIATE DISCRETE DATA.

Statistica Sinica·2013
Same author

Some exact tests for manifest properties of latent trait models.

Computational statistics & data analysis·2011
See all related articles

Related Experiment Video

Updated: Jun 12, 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

Testing nonlinearity of heavy-tailed time series.

Jan G De Gooijer1

  • 1Amsterdam School of Economics, University of Amsterdam, Amsterdam, The Netherlands.

Journal of Applied Statistics
|September 18, 2024
PubMed
Summary

This study introduces a new nonlinearity test for heavy-tailed time series, outperforming existing methods. The test uses Gini-based autocorrelations for improved accuracy in detecting nonlinear patterns in financial and network data.

Keywords:
62F0362M1062P05Gini-based autocorrelationheavy tailsnonlinear Pareto-type modelsnonlinearity testssub-sample stability

More Related Videos

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Related Experiment Videos

Last Updated: Jun 12, 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: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Area of Science:

  • Time Series Analysis
  • Statistical Inference
  • Econometrics

Background:

  • Heavy-tailed time series processes are common in finance, insurance, and network traffic.
  • Detecting nonlinearity in such series is crucial for accurate modeling and forecasting.
  • Existing nonlinearity tests may lack sufficient power for heavy-tailed data.

Purpose of the Study:

  • To develop a novel test statistic for detecting nonlinearity in heavy-tailed time series.
  • To evaluate the finite-sample performance of the proposed test.
  • To compare its efficacy against existing nonlinearity tests for heavy-tailed processes.

Main Methods:

  • Construction of a test statistic based on the sub-sample stability of Gini-based sample autocorrelations.
  • Monte Carlo simulations to assess finite-sample performance (size and power).
  • Comparison with a nonlinearity test utilizing a heavy-tailed analogue of conventional autocorrelation.

Main Results:

  • The proposed Gini-based autocorrelation test demonstrates superior size and power properties compared to the Resnick and Van den Berg (2000) test.
  • The test effectively distinguishes between linear and nonlinear processes in simulations.
  • Empirical application shows the test's utility on actuarial and Ethernet traffic data.

Conclusions:

  • The Gini-based autocorrelation test offers a more powerful and reliable method for nonlinearity detection in heavy-tailed time series.
  • This advancement has practical implications for analyzing complex real-world data with infinite variance properties.
  • The study validates the test's effectiveness through both simulated and empirical analyses.