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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

Exosomes secreted from GATA-4 overexpressing mesenchymal stem cells serve as a reservoir of anti-apoptotic microRNAs for cardioprotection.

International journal of cardiology·2015
Same author

[Etiology and pathogens of fungal endophthalmitis].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology·2015
Same author

Eucommia ulmoides Oliv. bark aqueous extract inhibits osteoarthritis in a rat model of osteoarthritis.

Journal of ethnopharmacology·2015
Same author

Aucubin prevents interleukin-1 beta induced inflammation and cartilage matrix degradation via inhibition of NF-κB signaling pathway in rat articular chondrocytes.

International immunopharmacology·2015
Same author

Treatment with recombinant lubricin attenuates osteoarthritis by positive feedback loop between articular cartilage and subchondral bone in ovariectomized rats.

Bone·2015
Same author

Tet1-mediated DNA demethylation regulates neuronal cell death induced by oxidative stress.

Scientific reports·2015
Same journal

Costunolide ameliorates autoimmune uveitis by targeting USP15 to suppress TNF-α-induced retinal endothelial inflammation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A ligandable PNT domain establishes ERG as a directly targetable oncogenic driver in prostate cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Identification of cellular intermediates unveils unique enzymes for flagellar glycan biosynthesis in <i>Clostridioides difficile</i>.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

The structure of correlated variability reflects task-relevant information in sensory neurons.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Shared neurogenetic substrates of nonplanning impulsivity and procrastination.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

HIV-1 capsid interactions with Nuclear Pore Complex components support nuclear entry via affinity gradient.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: May 8, 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

Optimal M-estimation in high-dimensional regression.

Derek Bean1, Peter J Bickel, Noureddine El Karoui

  • 1Department of Statistics, University of California, Berkeley, CA 94720, USA.

Proceedings of the National Academy of Sciences of the United States of America
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for optimizing regression objective functions using M-estimates in high-dimensional statistics. The method accounts for problem dimensionality, revealing dimension-dependent objective functions.

Keywords:
prox functionrobust regression

Related Experiment Videos

Last Updated: May 8, 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

Area of Science:

  • Statistics
  • Machine Learning

Background:

  • The classic problem of optimizing objective functions in regression is revisited.
  • M-estimates are a robust method for regression analysis.
  • High-dimensional statistics presents unique challenges for optimization.

Purpose of the Study:

  • To propose an algorithm for computing optimal objective functions in regression using M-estimates.
  • To address the challenges of high dimensionality in statistical optimization.
  • To identify dimension-dependent objective functions.

Main Methods:

  • Development of a novel algorithm for objective function optimization.
  • Analysis of the algorithm's performance under high-dimensional settings.
  • Consideration of known error distributions in regression models.

Main Results:

  • An algorithm is proposed that accounts for the dimensionality of the regression problem.
  • The analysis yields interesting families of dimension-dependent objective functions.
  • Optimality is achieved under specific, though not always satisfied, assumptions on the design matrix.

Conclusions:

  • The proposed algorithm offers a method for optimizing objective functions in high-dimensional regression.
  • Dimension-dependent objective functions are identified, offering new insights.
  • The study contributes to robust statistical methods in high-dimensional data analysis.