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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
73.1K
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

295
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...
295
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

449
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
449
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

37
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
37
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

619
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
619
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.0K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Discrepancies between self-reported alcohol use and breathalyzer results and factors associated with blood alcohol concentration positivity among road traffic injury patients in Cameroon.

Addiction (Abingdon, England)·2026
Same author

Impact of the COVID-19 pandemic on reported malaria incidence in children under five years of age in Cameroon: an interrupted time series analysis with regional heterogeneity (2017-2022).

Malaria journal·2026
Same author

Epidemiological patterns of motorcycle-related injuries in Cameroon: A comparative analysis of motorcycle users and pedestrians.

PLOS global public health·2026
Same author

Epidemiology of HIV in Remote Equatorial Regions of Cameroon: High Prevalence in Older Adults and Regional Disparities.

Tropical medicine and infectious disease·2025
Same author

Community willingness to participate in prehospital injury care: A cross-sectional survey of injury-prone areas along the national 3 highway in Cameroon.

PloS one·2025
Same author

Uptake of Intermittent Preventive Treatment and Its Associated Factors Among Pregnant Women in Cameroon: A Cross-Sectional Study.

Cureus·2025

Related Experiment Video

Updated: May 21, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

6.8K

An iterative matrix uncertainty selector for high-dimensional generalized linear models with measurement errors.

Betrand Fesuh Nono1, Georges Nguefack-Tsague2, Martin Kegnenlezom3

  • 1National Advanced School of Engineering, University of Yaoundé I, Cameroon.

Statistical Methods in Medical Research
|March 19, 2025
PubMed
Summary

A new method, the Iterative Matrix Uncertainty Selector (IMUS), offers effective variable selection for high-dimensional regression with measurement errors. IMUS is an error distribution-free approach that performs well in simulations and real-world data analysis.

Keywords:
Generalized linear modelgene expressionhigh-dimensional dataiterative re-weighted least squaresmatrix uncertainty selectormeasurement error

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K
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

2.0K

Related Experiment Videos

Last Updated: May 21, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

6.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K
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

2.0K

Area of Science:

  • Statistics
  • Bioinformatics
  • Computational Biology

Background:

  • Measurement error is a significant challenge in high-dimensional generalized linear regression.
  • Existing regularization methods often struggle with measurement error, requiring computationally intensive error distribution estimation.
  • There is a need for robust, error distribution-free variable selection techniques.

Purpose of the Study:

  • To introduce the Iterative Matrix Uncertainty Selector (IMUS), a novel error distribution-free method for variable selection in high-dimensional generalized linear regression.
  • To evaluate the performance of IMUS compared to existing methods in simulations and real-world datasets.
  • To provide an efficient and reliable tool for addressing measurement error in regression analysis.

Main Methods:

  • Developed the Iterative Matrix Uncertainty Selector (IMUS) based on the matrix uncertainty selector framework.
  • Implemented an efficient iterative algorithm applicable to generalized linear models within the exponential family.
  • Validated IMUS through simulations in logistic and Poisson regression and on three microarray gene expression datasets.

Main Results:

  • IMUS demonstrated effective covariate selection with smoother convergence and clearer elbow criteria than other error distribution-free methods.
  • Simulation studies showed IMUS performed comparably to Generalized Matrix Uncertainty Selector (GMUS) and Generalized Matrix Uncertainty Lasso (GMUL) in covariate selection.
  • IMUS exhibited smaller estimation errors and superior convergence properties on microarray datasets compared to GMUS and GMUL, which faced convergence issues or lacked clear selection criteria.

Conclusions:

  • IMUS provides a robust and efficient error distribution-free approach for variable selection in high-dimensional generalized linear regression with measurement errors.
  • The method offers practical advantages, including smoother convergence and well-defined selection criteria, making it suitable for complex biological data.
  • IMUS presents a promising solution for overcoming challenges posed by measurement error in statistical modeling.