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...
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)...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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.
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

You might also read

Related Articles

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

Sort by
Same author

Treatment of High-Risk Idiopathic Membranous Nephropathy with Huaier Granules and RASi: A Case Series.

International medical case reports journal·2026
Same author

Med-Diet: evaluation of an LLM-based system for clinically guided nutrition care in chronic diseases.

Frontiers in nutrition·2026
Same author

A pathological morphology parameter-based prognostic nomogram for high-risk prostate cancer patients treated with neoadjuvant therapy followed by radical prostatectomy: a retrospective study.

World journal of surgical oncology·2026
Same author

[Comparison of Imaging Efficacy and Patient Tolerability Between a Novel Cellulose-Based anda Conventional Starch-Based Oral Contrast Agent: A Prospective Randomized Controlled Trial].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae·2026
Same author

Author Correction: Hospital information system based psychological nursing improves maternal and neonatal outcomes in cesarean section patients.

Scientific reports·2026
Same author

Triple-Responsive Hydrogel Integrating Polyphenol-Metal Activity for Infected Diabetic Wound Therapy.

ACS applied materials & interfaces·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 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

Variable Selection for Partially Linear Models with Measurement Errors.

Hua Liang, Runze Li

    Journal of the American Statistical Association
    |January 5, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces new variable selection methods for partially linear models with measurement errors. These penalized regression techniques offer accurate estimation, performing comparably to oracle procedures.

    More Related Videos

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
    06:48

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

    Published on: June 25, 2019

    Related Experiment Videos

    Last Updated: Jun 17, 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

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
    06:48

    Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

    Published on: June 25, 2019

    Area of Science:

    • Statistics
    • Econometrics

    Background:

    • Partially linear models are widely used in statistical modeling.
    • Covariate measurement error can bias variable selection results.

    Purpose of the Study:

    • To develop robust variable selection methods for partially linear models with additive measurement errors.
    • To investigate the theoretical properties and practical performance of the proposed methods.

    Main Methods:

    • Proposing two variable selection procedures: penalized least squares and penalized quantile regression.
    • Utilizing nonconvex penalized principles with bias correction techniques (correction-for-attenuation and orthogonal regression).
    • Analyzing sampling properties, including convergence rates and asymptotic normality.

    Main Results:

    • Establishing theoretical guarantees for the proposed variable selection procedures.
    • Demonstrating that the methods achieve oracle property under specific conditions.
    • Evaluating finite sample performance through Monte Carlo simulations.

    Conclusions:

    • The proposed penalized regression methods effectively perform variable selection in the presence of measurement error.
    • These methods offer a robust alternative to existing techniques for partially linear models.
    • The study provides a comprehensive analysis of theoretical and practical aspects.