<i>L</i><sub>1</sub>penalty and shrinkage estimation in partially linear models with random coefficient autoregressive errors
Bibliographic record
Abstract
In partially linear models, we consider methodology for simultaneous model selection and parameter estimation with random coefficient autoregressive errors by using lasso and shrinkage strategies. We provide natural adaptive estimators that significantly improve upon the classical procedures in the situation where some of the predictors are nuisance variables that may or may not affect the association between the response and the main predictors. In the context of two competing partially linear regression models (full and submodels), we consider an adaptive shrinkage estimation strategy and propose the shrinkage estimator and the positive‐rule shrinkage estimator. We develop the properties of these estimators by using the notion of asymptotic distributional risk. The shrinkage estimators are shown to have a higher efficiency than the classical estimators for a wide class of models. For the lasso‐type estimation strategy, we devise efficient algorithms to obtain numerical results. We compare the relative performance of lasso with the shrinkage estimator and the other estimators. Monte Carlo simulation experiments are conducted for various combinations of the nuisance parameters and sample size, and the performance of each method is evaluated in terms of simulated mean squared error. The comparison reveals that lasso and shrinkage strategies outperform the classical procedure. The relative performance of lasso and shrinkage strategies is comparable. The shrinkage estimators perform better than the lasso strategy in the effective part of the parameter space when, and only when, there are many nuisance variables in the model. A data example is showcased to illustrate the usefulness of suggested methods. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".