Minimum description length principle for linear mixed effects models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The minimum description length (MDL) principle originated from data compression literature and has been considered for deriving statistical model selection procedures. Most of the existing methods that use the MDL principle focus on models with independent data, particularly in the context of linear regression. This paper considers data with repeated measurements andstudies theselection of fixed effect covariates forlinear mixed effect models. We propose a class of MDL procedures which incorporate the dependence structure within individual or cluster and uses data-adaptive penalties that suit both finite and infinite dimensional data generating mechanisms. Theoretical justifications are provided from both data compression and statistical perspectives, where the covariance of random effects is treated as known or estimated by maximum likelihood. Numerical experiments are conducted to demonstrate the usefulness of the proposed MDL procedure and the influence of the estimated covariance, and an application to U.S. EPA data for air quality control is provided.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it