Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
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...
226
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

1.1K
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...
1.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

264
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...
264
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Prediction Intervals01:03

Prediction Intervals

3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.1K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
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...
8.9K

You might also read

Related Articles

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

Sort by
Same author

[A quantitative trabecular structural analysis using X-ray micro CT in ovariectomized rats].

Guang pu xue yu guang pu fen xi = Guang pu·2009
Same author

Error analysis of Cm measurement under the whole-cell patch-clamp recording.

Journal of neuroscience methods·2009
Same author

Understanding the self-assembly of charged nanoparticles at the water/oil interface.

Physical chemistry chemical physics : PCCP·2009
Same author

[Development of new SSR markers from EST of SSH cDNA libraries on rose fragrance].

Yi chuan = Hereditas·2009
Same author

Crocin and geniposide profiles and radical scavenging activity of gardenia fruits (Gardenia jasminoides Ellis) from different cultivars and at the various stages of maturation.

Fitoterapia·2009
Same author

Small-molecule screening using a human primary cell model of HIV latency identifies compounds that reverse latency without cellular activation.

The Journal of clinical investigation·2009
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
See all related articles
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 Experiment Video

Updated: Jan 10, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K

On estimation and prediction for spatial generalized linear mixed models.

Hao Zhang1

  • 1Program in Statistics, Washington State University, Pullman 99164-3144, USA. zhanghao@wsu.edu

Biometrics
|March 14, 2002
PubMed
Summary
This summary is machine-generated.

This study introduces a linear prediction method for spatial generalized linear mixed models (GLMMs). The approach enhances random effect prediction accuracy for non-Gaussian spatial data, aiding precision agriculture applications.

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

3.7K
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

11.0K

Related Experiment Videos

Last Updated: Jan 10, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K
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

3.7K
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

11.0K

Area of Science:

  • Spatial statistics
  • Statistical modeling
  • Geostatistics

Background:

  • Spatial generalized linear mixed models (GLMMs) are crucial for analyzing non-Gaussian spatial data.
  • Predicting random effects in spatial GLMMs is essential for practical applications.
  • Existing methods may lack efficiency or accuracy in predicting random effects.

Purpose of the Study:

  • To develop an efficient and accurate method for predicting random effects in spatial GLMMs.
  • To demonstrate the linear nature of minimum mean-squared error (MMSE) prediction in spatial GLMMs.
  • To apply the developed method to real-world data for precision agriculture.

Main Methods:

  • Utilized spatial generalized linear mixed models (GLMMs) for non-Gaussian spatial variables.
  • Developed a linear prediction approach analogous to linear kriging for MMSE prediction of random effects.
  • Implemented a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation.

Main Results:

  • Showed that MMSE prediction of random effects in spatial GLMMs can be performed linearly.
  • The Monte Carlo EM algorithm provides MMSE estimates for realized random effects.
  • Successfully applied the method to a plant root disease dataset.

Conclusions:

  • The proposed linear prediction method offers an efficient way to estimate random effects in spatial GLMMs.
  • This technique facilitates the creation of accurate disease severity maps for precision agriculture.
  • The study bridges theoretical statistical advancements with practical agricultural applications.