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

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

825
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...
825
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

6.9K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
6.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

5.7K
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).
5.7K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.6K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.6K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

393
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...
393
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

409
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 0s. In...
409

You might also read

Related Articles

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

Sort by
Same author

The neutrophil-to-lymphocyte ratio and incident chronic kidney disease in a community-based cohort: a prospective study.

Scientific reports·2026
Same author

Transfer Learning for Moderate-Dimensional Ridge-Regularized Robust Linear Regression.

Entropy (Basel, Switzerland)·2026
Same author

Global research landscape and multisystem health mechanisms of luteolin: a comprehensive bibliometric and network pharmacology study.

Frontiers in nutrition·2026
Same author

Comment on "Prevalence of hypertension and related factors among suspected hypertensive medical personnel during COVID-19 vaccination".

Hypertension research : official journal of the Japanese Society of Hypertension·2026
Same author

Letter to the editor: "Different microcirculatory patterns in patients with COVID-19 and non-COVID-19 ARDS: A multicenter cross-sectional study".

Journal of critical care·2026
Same author

GPMassSimulator: A Graphormer-Based Method for Glycopeptide MS/MS Spectra Prediction.

Analytical chemistry·2025

Related Experiment Video

Updated: Nov 27, 2025

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

14.6K

Robustness Property of Robust-BD Wald-Type Test for Varying-Dimensional General Linear Models.

Xiao Guo1, Chunming Zhang2

  • 1Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a robust Wald-type test for general linear models, ensuring stable statistical inference even with contaminated data. The proposed robust-BD test maintains accuracy and power against data imperfections.

Keywords:
Bregman divergenceWald-type testgeneral linear modelhypothesis testinginfluence functionrobust

More Related Videos

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

3.6K
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

2.4K

Related Experiment Videos

Last Updated: Nov 27, 2025

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting
08:13

Robust Comparison of Protein Levels Across Tissues and Throughout Development Using Standardized Quantitative Western Blotting

Published on: April 9, 2019

14.6K
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

3.6K
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

2.4K

Area of Science:

  • Statistics
  • Econometrics
  • Data Science

Background:

  • Robust inference is crucial for reliable statistical analysis, especially with imperfect data.
  • Existing methods for robust inference are often limited to finite-dimensional settings and specific loss functions.
  • Contaminated data can significantly impact the stability of test statistics' asymptotic level and power.

Purpose of the Study:

  • To investigate the stability of asymptotic level and power of test statistics with contaminated data in general linear models.
  • To introduce and analyze a novel robust Wald-type test using robust error measures (robust-BD) for diverging parameter dimensions.
  • To assess the robustness of validity and efficiency of the proposed test under various contamination scenarios.

Main Methods:

  • Derivation of the influence function for the robust-BD parameter estimator under regularity conditions.
  • Asymptotic analysis of the robust-BD Wald-type test statistic.
  • Evaluation of the test's performance under small data contamination and in the neighborhood of contiguous alternatives.

Main Results:

  • The robust-BD parameter estimator's influence function was successfully derived.
  • The robust-BD Wald-type test demonstrates asymptotic robustness of validity, maintaining a stable level under null hypothesis contamination.
  • The test exhibits sufficient asymptotic power under contaminated distributions near contiguous alternatives.

Conclusions:

  • The proposed robust-BD Wald-type test offers a reliable solution for statistical inference in general linear models with contaminated data.
  • The findings support the practical utility of the robust-BD test, showing its stability and efficiency in challenging data conditions.
  • This research extends robust inference methodologies to higher-dimensional settings and a broader class of error measures.