Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Errors In Hypothesis Tests

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

Bonferroni Test

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

Sign Test for Matched Pairs

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...

Comparing Experimental Results: Student's t-Test

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...

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

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

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 data...

You might also read

Related Articles

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

Sort by

Same author

Modeling of a pulse-like release of Cr(VI) in a contaminated site during an extreme rain event based on HHMM.

Environmental geochemistry and health·2026

Same author

Staged SWAT calibration with bias-corrected precipitation product for enhancing flow data continuity in tributaries of the Mekong River.

The Science of the total environment·2024

Same author

Non-monotonic changes in Asian Water Towers' streamflow at increasing warming levels.

Nature communications·2023

Same author

Increasing compound events of extreme hot and dry days during growing seasons of wheat and maize in China.

Scientific reports·2018

Same author

Bahadur representations of M-estimators and their applications in general linear models.

Journal of inequalities and applications·2018

Same author

Comparing different methods for determining forest evapotranspiration and its components at multiple temporal scales.

The Science of the total environment·2018

Search research articles

Related Experiment Video

Updated: Jul 12, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

M-test in linear models with negatively superadditive dependent errors.

Yuncai Yu¹, Hongchang Hu², Ling Liu³

¹State Key Laboratory of Mechanics and Control of Mechanical Structures, Institute of Nano Science and Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China.

Journal of Inequalities and Applications

|October 14, 2017

Summary

This study introduces a robust M-test for linear models with negatively superadditive dependent (NSD) errors. The new method effectively tests regression parameters, offering stable estimates and reliable test power.

Keywords:

M-test Monte Carlo simulations NSD random sequences asymptotic property linear regression models

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: Jul 12, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

Statistics
Econometrics
Probability Theory

Background:

Linear models are widely used in statistical analysis.
Dependent errors can violate standard assumptions, affecting hypothesis testing.
Negatively superadditive dependence (NSD) is a specific type of error structure.

Purpose of the Study:

To develop a robust statistical test for regression parameters in linear models.
To address the challenge of testing hypotheses when errors exhibit negative superadditive dependence (NSD).
To provide a method that is reliable even with complex error structures.

Main Methods:

A robust M-test based on the M-criterion is proposed.
Asymptotic distribution of the test statistic is derived.
Consistent estimation of redundancy parameters is established.

Main Results:

The study establishes the asymptotic distribution of the proposed M-test statistic.
Consistent estimates for parameters within the asymptotic distribution are obtained.
Monte Carlo simulations demonstrate the stability of parameter estimates and the power of the test.

Conclusions:

The proposed M-test is a robust method for hypothesis testing in linear models with NSD errors.
The method provides stable parameter estimates and demonstrates good power.
The findings are validated through simulations under various conditions.