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

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.4K
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.
4.4K
Econometric Views (EViews)01:29

Econometric Views (EViews)

234
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
234
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
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...
1.8K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
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...
6.4K
Correlation of Experimental Data01:23

Correlation of Experimental Data

266
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
266
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
2.1K

You might also read

Related Articles

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

Sort by
Same author

A safe and anti-inflammatory plant-derived nanovesicle platform for targeted delivery in acute lung injury.

Bioactive materials·2026
Same author

Training effect of a deep learning-based blended teaching model on ECMO transport for ICU nurses: a prospective, parallel-group, randomized controlled trial.

BMC nursing·2026
Same author

Oxidative stress and endoplasmic reticulum stress in acute kidney injury: mechanistic crosstalk and therapeutic modulation.

Frontiers in medicine·2026
Same author

Enhanced Anti-Tumor Activity of Cetuximab-Modified Nanostructured Lipid Carriers Loaded with <i>Para</i>-Quinone Methide Derivative <i>p</i>-QM-1h.

International journal of molecular sciences·2026
Same author

Dietary <i>Bacillus subtilis</i> Group Reduces the General Infection of <i>Salmonella</i> Pullorum in Broiler Chicken.

Antibiotics (Basel, Switzerland)·2026
Same author

Metabolic engineering of Bacillus subtilis for high-yield surfactin production.

Biotechnology for biofuels and bioproducts·2026
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 30, 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

Testing for Serial Correlation in Autoregressive Exogenous Models with Possible GARCH Errors.

Hanqing Li1,2, Xiaohui Liu1,2, Yuting Chen3

  • 1School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Entropy (Basel, Switzerland)
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new serial correlation test for Autoregressive Exogenous (ARX) models. The novel method, based on profile empirical likelihood, demonstrates robust performance even with complex data conditions like heteroscedasticity.

Keywords:
ARX modelautocorrelationempirical likelihoodserial correlation

More Related Videos

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

10.8K
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

Related Experiment Videos

Last Updated: Aug 30, 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
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

10.8K
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

Area of Science:

  • Time Series Analysis
  • Econometrics
  • Statistical Modeling

Background:

  • Autoregressive Exogenous (ARX) models are standard for time series analysis, combining autoregressive and predictive regression components.
  • Existing diagnostic methods for ARX models often fail under real-world conditions like heteroscedasticity and cross-correlations.
  • These limitations necessitate advanced diagnostic tools for reliable ARX model evaluation.

Purpose of the Study:

  • To develop a novel serial correlation test for ARX models.
  • To address the limitations of current diagnostic methods in complex scenarios.
  • To enhance the validity and reliability of ARX model analysis in practical applications.

Main Methods:

  • A new serial correlation test was developed utilizing the profile empirical likelihood approach.
  • The proposed method was evaluated through extensive simulations.
  • The test's efficacy was further validated using two real-world datasets.

Main Results:

  • The proposed profile empirical likelihood-based serial correlation test demonstrated strong performance.
  • The method maintained good efficacy across various challenging conditions, including heteroscedasticity and error correlations.
  • Simulation results and real data examples confirmed the test's practical utility.

Conclusions:

  • The new serial correlation test offers a reliable diagnostic tool for ARX models.
  • It effectively handles complex data structures often encountered in real-time applications.
  • This method improves the robustness and applicability of ARX modeling in time series analysis.