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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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 number is...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

You might also read

Related Articles

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

Sort by
Same author

A compact low-power magnetic particle imaging scanner based on a permanent-magnet field-free-line generator with high gradient.

The Review of scientific instruments·2026
Same author

Sinomenine restrains the proliferation and hyperactivation of B lymphocytes partly by inhibiting interferon regulatory factor 5.

Journal of ethnopharmacology·2026
Same author

High-fidelity compressed high-speed imaging for resolving rapid micro-dynamics.

Optics express·2026
Same author

Atomically Regulated Symmetry-Breaking Sulfur-Bridged Dual Iron Sites Catalyst for High-Performance Oxygen Reduction Reaction.

Angewandte Chemie (International ed. in English)·2026
Same author

The efficacy of vitamin D supplementation in the management of childhood asthma: a systematic review and meta-analysis.

Frontiers in nutrition·2026
Same author

A wearable non-invasive sonogenetic pacemaker.

Nature biomedical engineering·2026
Same journal

A KL-divergence-based test for elliptical distribution.

Journal of nonparametric statistics·2026
Same journal

Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.

Journal of nonparametric statistics·2026
Same journal

A comparison of causal inference methods for evaluating multiple treatment groups.

Journal of nonparametric statistics·2025
Same journal

Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates.

Journal of nonparametric statistics·2025
Same journal

TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-Based Data.

Journal of nonparametric statistics·2025
Same journal

Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing.

Journal of nonparametric statistics·2025
See all related articles

Related Experiment Video

Updated: May 13, 2026

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

Weighted Quantile Regression for AR model with Infinite Variance Errors.

Zhao Chen1, Runze Li, Yaohua Wu

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

Journal of Nonparametric Statistics
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces weighted quantile regression and induced smoothing for autoregressive (AR) models with heavy-tailed, infinite variance errors. These methods offer robust statistical estimation and hypothesis testing for complex data.

Keywords:
Quantile regressionautoregressive modelinfinite variance

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: May 13, 2026

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

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Statistics
  • Econometrics
  • Time Series Analysis

Background:

  • Autoregressive (AR) models are widely used, but standard methods assume finite variance errors.
  • Heavy-tailed error distributions, common in scientific research, violate this assumption, necessitating advanced statistical techniques.
  • Infinite variance errors present unique challenges for traditional statistical estimation in AR models.

Purpose of the Study:

  • To develop robust statistical estimation methods for AR models with infinite variance errors.
  • To introduce a novel weighted quantile regression approach tailored for heavy-tailed error distributions.
  • To propose an induced smoothing technique to overcome computational difficulties in weighted quantile regression.

Main Methods:

  • Weighted quantile regression was employed to handle the challenges posed by infinite variance errors.
  • An induced smoothing method was developed to address computational complexities associated with weighted quantile regression.
  • A statistical test for linear hypotheses on regression coefficients was formulated.

Main Results:

  • The proposed weighted quantile regression and induced smoothing methods provide effective estimation for AR models with heavy-tailed errors.
  • The difference between the weighted quantile regression estimate and its smoothed version was found to be negligible.
  • Monte Carlo simulations demonstrated the good finite sample performance of the developed procedures.

Conclusions:

  • The proposed methodology offers a robust framework for analyzing AR models with infinite variance errors.
  • The induced smoothing technique effectively resolves computational issues in weighted quantile regression.
  • The study validates the practical applicability of the methods through real-life data analysis.