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 k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

227
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
227
What are Estimates?01:06

What are Estimates?

8.2K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.2K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.5K
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...
7.5K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.6K
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 +...
9.6K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.8K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Circulation of Coxsackievirus A6 in hand-foot-mouth disease in Guangzhou, 2010-2012.

Virology journal·2014
Same author

Thermoelectric Seebeck effect in oxide-based resistive switching memory.

Nature communications·2014
Same author

The role of low levels of fullerene C60 nanocrystals on enhanced learning and memory of rats through persistent CaMKII activation.

Biomaterials·2014
Same author

High open circuit voltage in regioregular narrow band gap polymer solar cells.

Journal of the American Chemical Society·2014
Same author

Small RNAs: a new paradigm in plant-microbe interactions.

Annual review of phytopathology·2014
Same author

Time-effect relationship of extracts from ginseng, notoginseng and chuanxiong on vascular endothelial cells senescence.

Chinese journal of integrative medicine·2014
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Modified robust variance estimator for generalized estimating equations with improved small-sample performance.

Ming Wang1, Qi Long

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. mwang36@emory.edu

Statistics in Medicine
|May 4, 2011
PubMed
Summary
This summary is machine-generated.

A new robust variance estimator improves generalized estimating equations (GEE) performance, especially with small sample sizes. This novel method offers better efficiency and accuracy for correlated data analysis.

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

938
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Related Experiment Videos

Last Updated: Jan 27, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

938
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Generalized estimating equations (GEE) are widely used for analyzing correlated or clustered data.
  • The standard robust sandwich estimator for GEE variance is sensitive to small sample or cluster sizes.
  • Existing modifications to the sandwich estimator have limitations.

Purpose of the Study:

  • To propose a novel robust variance estimator for GEE.
  • To improve the finite sample performance and efficiency of GEE.
  • To address the limitations of existing variance estimators in GEE.

Main Methods:

  • Developed a new robust variance estimator by combining existing modifications.
  • Conducted theoretical derivations and numerical simulations.
  • Applied the proposed method to a dental study dataset.

Main Results:

  • The proposed estimator demonstrates superior efficiency and finite sample performance.
  • It exhibits lower bias and improved confidence interval coverage rates in small samples.
  • Numerical results validate the theoretical findings.

Conclusions:

  • The novel robust variance estimator enhances GEE analysis, particularly in scenarios with limited data.
  • It offers a more reliable approach for estimating variance-covariance matrices in GEE.
  • The method provides improved accuracy and coverage for regression coefficient estimates.