Reduction of Restricted Maximum Likelihood for Random Coefficient Models
Kurt S. Riedel
TL;DR
This paper presents a reformulation of the REML estimator for the dispersion matrix in random coefficient models, utilizing sufficient statistics from individual regressions to improve understanding or computation.
Contribution
It introduces a new way to express the REML estimator using sufficient statistics, offering potential computational or theoretical advantages.
Findings
Simplifies the REML estimation process.
Provides a new perspective on the dispersion matrix estimation.
Potentially improves computational efficiency.
Abstract
The restricted maximum likelihood (REML) estimator of the dispersion matrix for random coefficient models is rewritten in terms of the sufficient statistics of the individual regressions.
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Taxonomy
TopicsSoil Geostatistics and Mapping · Statistical Methods and Bayesian Inference · Soybean genetics and cultivation