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

Structured additive regression for categorical space-time data: a mixed model approach.

Thomas Kneib1, Ludwig Fahrmeir

  • 1Department of Statistics, University of Munich, D-80539 Munich, Germany. Thomas.Kneib@stat.uni-muenchen.de

Biometrics
|March 18, 2006
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

Methodological guidance on clinical prediction models in mental health research.

Psychological medicine·2026
Same author

Satellite data show trees delay budburst across landscapes to escape herbivores.

Nature ecology & evolution·2026
Same author

Flexible Bayesian modeling of non-equidispersed counts with penalized complexity priors in disease incidence studies.

Statistical methods in medical research·2026
Same author

Letter to the editor refuting "Debunking the GAMLSS myth: Simplicity reigns in pulmonary function diagnostics".

Respiratory medicine·2025
Same author

Bayesian competing risks survival modeling for assessing the cause of death of patients with heart failure.

The international journal of biostatistics·2025
Same author

Probabilistic Topic Modeling With Transformer Representations.

IEEE transactions on neural networks and learning systems·2025

This study introduces structured additive regression models for categorical forest health data. These models flexibly incorporate nonlinear effects, time trends, and spatial patterns for improved tree damage assessment.

Area of Science:

  • Ecology and Environmental Science
  • Statistical Modeling
  • Forestry Science

Background:

  • Forest health assessments are crucial for ecological monitoring and management.
  • Existing statistical models may not adequately capture complex space-time dynamics of forest damage.
  • There is a need for flexible modeling approaches to analyze categorical forest health data.

Purpose of the Study:

  • To propose a general class of structured additive regression models for categorical responses in space-time forest health studies.
  • To allow for flexible semiparametric predictors, including nonlinear effects, time trends, and spatial dependencies.
  • To provide a Bayesian framework for inference and compare it with frequentist approaches.

Main Methods:

  • Utilized penalized splines to model nonlinear effects of continuous covariates, time trends, and interactions.

Related Experiment Videos

  • Incorporated spatial effects using Markov random fields, Gaussian random fields, or 2D penalized splines.
  • Developed an empirical Bayes method based on a categorical linear mixed model, related to penalized likelihood estimation.

    • Main Results:

    • Simulation studies evaluated the performance of different spatial effect choices and compared the empirical Bayes approach.
    • Investigated the bias of mixed model estimates for variance components.
    • Demonstrated the application of the proposed models using forest health survey data.

    Conclusions:

    • The proposed structured additive regression models offer a flexible and robust framework for analyzing space-time categorical data in forest health studies.
    • The empirical Bayes approach provides a computationally efficient method for parameter estimation.
    • The methodology allows for the simultaneous modeling of various complex effects influencing forest health.