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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

719
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
719
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
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

814
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...
814
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
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...
8.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.0K
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 +...
9.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

134
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...
134

You might also read

Related Articles

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

Sort by
Same author

Heart failure hospitalization in patients with and without type 2 diabetes: A population-based retrospective cohort study.

PloS one·2026
Same author

The neutrophil-to-lymphocyte ratio and incident chronic kidney disease in a community-based cohort: a prospective study.

Scientific reports·2026
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026
Same author

Auricular Point Acupressure Self-Management (APA-SM) program for chronic musculoskeletal pain among rural populations: a protocol for a pragmatic, randomized controlled trial.

BMJ open·2026
Same author

Glaucoma Risk with Metformin and Sulfonylurea Therapies in Type 2 Diabetes: A Retrospective Cohort Study.

Clinical ophthalmology (Auckland, N.Z.)·2026
Same author

Smartphone-Based Ecological Momentary Assessment of Pain in Older Adults Undergoing Auricular Point Acupressure for Chronic Low Back Pain: Secondary Analysis of a Randomized Controlled Trial.

JMIR formative research·2026
Same journal

A KL-divergence-based test for elliptical distribution.

Journal of nonparametric statistics·2026
Same journal

Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.

Journal of nonparametric statistics·2026
Same journal

A comparison of causal inference methods for evaluating multiple treatment groups.

Journal of nonparametric statistics·2025
Same journal

Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates.

Journal of nonparametric statistics·2025
Same journal

TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-Based Data.

Journal of nonparametric statistics·2025
Same journal

Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing.

Journal of nonparametric statistics·2025
See all related articles

Related Experiment Video

Updated: Oct 24, 2025

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

3.5K

Efficient Semiparametric Regression for Longitudinal Data with Regularized Estimation of Error Covariance Function.

Shengji Jia1, Chunming Zhang1, Hulin Wu2

  • 1Department of Statistics, University of Wisconsin-Madison, WI, USA.

Journal of Nonparametric Statistics
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new regularization method for estimating covariance functions in longitudinal data analysis. The approach improves estimation efficiency for regression coefficients, especially with irregular or unbalanced time points.

Keywords:
Covariance functionSobolov spacelocal linear regressionmethod of regularizationprofile weighted least squaressemiparametric varying-coefficient partially linear modeltensor product space

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.5K

Related Experiment Videos

Last Updated: Oct 24, 2025

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

3.5K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.5K

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Estimating regression coefficients efficiently is crucial for longitudinal data analysis.
  • Challenges exist in estimating the covariance matrix for data collected at irregular or unbalanced time points.
  • Existing methods struggle with the complexities of irregularly sampled longitudinal data.

Purpose of the Study:

  • To develop a regularization method for estimating the covariance function in longitudinal data.
  • To create an efficient stepwise procedure for estimating parametric components in varying-coefficient models.
  • To address challenges in covariance matrix estimation for irregularly sampled data.

Main Methods:

  • A regularization method is proposed for estimating the covariance function.
  • A stepwise procedure is developed for efficient estimation of parametric components.
  • The method is applied to varying-coefficient partially linear models and varying-coefficient temporal mixed effects models.

Main Results:

  • The proposed method utilizes the covariance function structure for faster convergence rates.
  • Simulation studies demonstrate superior performance compared to existing approaches.
  • The procedure is shown to be easy to implement and effective on both simulated and real data.

Conclusions:

  • The developed regularization and stepwise procedure enhance estimation efficiency for longitudinal data.
  • The method offers a robust solution for covariance matrix estimation with irregular time points.
  • This approach provides a practical and performant tool for analyzing complex longitudinal datasets.