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

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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...
Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

You might also read

Related Articles

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

Sort by
Same author

Urinary proteomic profiling identifies candidate biomarkers for pediatric MAFLD in comparison with obesity and healthy controls.

BMC pediatrics·2026
Same author

Bacitracin-regulated injectable PHEMA hydrogels with intrinsic mild negative swelling for soft tissue repair.

Materials horizons·2026
Same author

Hybrid Supervised-Unsupervised Modeling for Post-Hurricane Private Well Contamination Risk Score Using Empirical Validation and Community-Informed Assessment.

GeoHealth·2026
Same author

Tetrahydroxy Diboron-Enabled 3D-Printable Bioactive Hydrogel Scaffolds for Accelerated Repair of Vaginal Defects.

Advanced healthcare materials·2026
Same author

Association of <i>SNHG17/CDKN2B-AS1</i> polymorphisms with salpingitis and fallopian tube carcinoma susceptibility in the Chinese Han population.

Gynecology and pelvic medicine·2026
Same author

Single-cell genomic analysis of cancer cells from one treatment-naïve patient with metastatic prostate cancer.

BMC genomic data·2026
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
See all related articles

Related Experiment Video

Updated: May 9, 2026

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

Corrected-loss estimation for quantile regression with covariate measurement errors.

Huixia Judy Wang1, Leonard A Stefanski, Zhongyi Zhu

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A. , judy_wang@ncsu.edu.

Biometrika
|July 12, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for quantile regression with measurement errors in covariates. The approach simplifies estimation by relaxing restrictive assumptions, improving efficiency over existing techniques.

Keywords:
Corrected loss functionLaplace distributionMeasurement errorNormal distributionQuantile regressionSmoothing

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 9, 2026

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

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
  • Biostatistics

Background:

  • Covariate measurement error poses challenges in quantile regression analysis.
  • Existing methods often require strong assumptions, limiting their applicability and computational feasibility.

Purpose of the Study:

  • To develop a novel, flexible, and computationally simple estimation approach for quantile regression with covariate measurement errors.
  • To relax stringent assumptions imposed by traditional methods.

Main Methods:

  • A corrected-score estimation approach is proposed to handle covariate measurement errors.
  • The method requires only the linearity of the specific quantile function of interest.

Main Results:

  • The proposed method is simple to implement and does not require parametric assumptions on error distributions.
  • Finite-sample simulations show the new estimators are more efficient than existing methods across various models.

Conclusions:

  • The developed method offers a more flexible and efficient solution for quantile regression in the presence of covariate measurement errors.
  • This approach broadens the applicability of quantile regression in statistical modeling.