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 Experiment Videos

Modelling balanced longitudinal data: maximum likelihood estimation and analysis of variance.

A Forcina1

  • 1Dip. di Scienze Statistiche, Perugia, Italy.

Biometrics
|September 1, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Sustainability for ship operations in seaport areas: Technical solutions and environmental assessment.

The Science of the total environment·2025
Same author

Splanchnic vein thrombosis in myeloproliferative neoplasms: risk factors for recurrences in a cohort of 181 patients.

Blood cancer journal·2016
Same author

Control of infectious mortality due to carbapenemase-producing Klebsiella pneumoniae in hematopoietic stem cell transplantation.

Bone marrow transplantation·2016
Same author

High rate of recurrent venous thromboembolism in patients with myeloproliferative neoplasms and effect of prophylaxis with vitamin K antagonists.

Leukemia·2016
Same author

Coadministration of posaconazole and sirolimus in allogeneic hematopoietic stem cell transplant recipients.

Bone marrow transplantation·2016
Same author

Early recovery of CMV immunity after HLA-haploidentical hematopoietic stem cell transplantation as a surrogate biomarker for a reduced risk of severe infections overall.

Bone marrow transplantation·2015
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
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study simplifies covariance parameter estimation in linear models by leveraging regression parameter estimates. An analysis of variance table offers a practical alternative to complex likelihood inference for these models.

Area of Science:

  • Statistics
  • Statistical Modeling
  • Linear Models

Background:

  • Covariance structures in linear models often include measurement error, serial correlation, and between-unit variation.
  • Accurate estimation of these covariance parameters is crucial for valid statistical inference.
  • Maximum likelihood estimation (MLE) can be computationally intensive for complex covariance structures.

Purpose of the Study:

  • To simplify the maximum likelihood estimation (MLE) of covariance parameters in linear models.
  • To explore the utility of an analysis of variance (ANOVA) table as an alternative to likelihood inference.
  • To provide practical examples of these methods.

Main Methods:

  • Utilized results from explicit estimation of regression parameters.

Related Experiment Videos

  • Applied these results to simplify the MLE of covariance parameters.
  • Employed an analysis of variance (ANOVA) table for comparative inference.

    • Main Results:

    • The proposed method simplifies the estimation of covariance parameters.
    • The ANOVA table approach provides a computationally less intensive alternative to MLE.
    • Demonstrated the applicability of the methods through two illustrative examples.

    Conclusions:

    • Explicit estimation of regression parameters offers a pathway to simplified covariance parameter estimation in linear models.
    • Analysis of variance tables can serve as a viable and simpler alternative to likelihood inference for certain covariance structures.
    • The findings facilitate more accessible statistical analysis in the presence of complex error structures.