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

45
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...
45
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

439
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...
439
Multiple Regression01:25

Multiple Regression

3.0K
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...
3.0K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

181
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
181
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
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...
33
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

111
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
111

You might also read

Related Articles

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

Sort by
Same author

Marginal and internal fit of CAD/CAM restorations with CAD-defined cement space: a meta-analysis of micro-CT studies.

BMC oral health·2026
Same author

Clinical predictors of relapse and severe disease phenotype in children with non-systemic juvenile idiopathic arthritis.

European journal of pediatrics·2026
Same author

Rates of incidental prostate cancer following HoLEP: Can it be predicted preoperatively?

Canadian Urological Association journal = Journal de l'Association des urologues du Canada·2026
Same author

The impact of the COVID-19 pandemic on scientific publications in the field of hernia surgery: a brief bibliometric analysis.

Hernia : the journal of hernias and abdominal wall surgery·2026
Same author

Time-Varying Path-Specific Direct and Indirect Effects: A Novel Approach to Examine Dynamic Behavioral Processes with Application to Smoking Cessation.

Multivariate behavioral research·2026
Same author

Semi-parametric benchmark dose analysis with monotone additive models.

Biometrics·2024
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

A computationally efficient sequential regression imputation algorithm for multilevel data.

Tugba Akkaya Hocagil1, Recai M Yucel2

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.

Journal of Applied Statistics
|August 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a computationally efficient imputation method for missing data, significantly improving speed and accuracy in high-dimensional analyses. The new approach enhances inferential quality and reduces computation time for complex datasets.

Keywords:
Sequential regression imputationcomputational efficiencyfast variable by variable imputationmultilevel datamultiple imputation by chained equations

More Related Videos

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

3.3K
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

10.7K

Related Experiment Videos

Last Updated: Jun 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

3.3K
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

10.7K

Area of Science:

  • Statistics
  • Computational Statistics
  • Biostatistics

Background:

  • Sequential imputation methods face computational challenges, particularly in high-dimensional data.
  • Existing variable-by-variable imputation algorithms can be computationally intensive.

Purpose of the Study:

  • To develop and evaluate a computationally advantageous imputation method.
  • To improve the efficiency and inferential quality of missing data imputation algorithms.
  • To apply the novel imputation method to a real-world public health problem.

Main Methods:

  • Adopted computationally advantageous methods by sampling missing data from their predictive distributions.
  • Developed a novel variable-by-variable imputation algorithm.
  • Conducted a comprehensive simulation study to assess computational performance.
  • Compared the proposed algorithm against commonly used alternatives.

Main Results:

  • The proposed imputation method demonstrated significantly improved computation time.
  • The algorithm showed a significant advantage in computational efficiency over existing methods.
  • The method also yielded superior inferential quality compared to alternatives.
  • The approach was successfully applied to investigate factors influencing poor birth outcomes in New York State.

Conclusions:

  • The novel imputation technique offers substantial computational benefits for high-dimensional data.
  • This method provides a more efficient and accurate alternative for missing data imputation.
  • The approach is effective for both theoretical assessment and application in substantive research problems.