MAXIMUM LIKELIHOOD INFERENCE IN ROBUST LINEAR MIXED-EFFECTS MODELS USING MULTIVARIATE t DISTRIBUTIONS
Bibliographic record
Abstract
This paper focuses on the problem of maximum likelihood estimation in linear mixed-effects models where outliers or unduly large observations are present in clustered or longitudinal data. Multivariate t distributions are often imposed on either random effects and/or random errors to incorporate outliers. A powerful algorithm of maximum by parts (MBP) proposed by Song, Fan and Kalbfleisch (2005) is implemented to obtain maximum likelihood estimators when the likeli- hood is intractable. The computational efficiency of the MBP a us to further apply a profile-likelihood technique for the estimation of the degrees of freedom in t-distributions. Comparison of the Akaike information criterion (AIC) among candidate models provides an objective criterion to determine whether outliers are influential on the quality of model fit. The proposed models and methods are illustrated through both simulation studies and data analysis examples, with com- parison to the existing EM-algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".