Absolute penalty and shrinkage estimation strategies in linear and partially linear models with correlated errors
Bibliographic record
Abstract
In this dissertation we propose shrinkage estimators and absolute penalty estimators (APEs) in linear models, partially linear models (PLM) and quasi-likelihood models. We study the asymptotic properties of shrinkage estimators both analytically and through simulation studies, and compare their performance with APEs. In Chapter 2, we propose shrinkage estimators for a multiple linear regression with first order random coefficient autoregressive (RCAR(1)) error term. We also present two APEs for this models which are modified versions of lasso and adaptive lasso estimators. We compare the performance of shrinkage estimators and APEs through the mean squared error criterion. Monte Carlo studies were conducted to compare the estimators in two situations: when p > n and when p < n. A data example is presented to illustrate the usefulness of the suggested methods. In Chapter 3, we develop shrinkage estimators for a PLM with RCAR(1) error term. The nonparametric function is estimated using a kernel function. We also compare the performance of shrinkage estimators with a modified version of lasso for correlated data. Monte Carlo studies were conducted to compare the behavior of the proposed estimators. A data example is presented to illustrate the application of the suggested methods. In Chapter 4, we propose pretest and shrinkage estimators for quasi-likelihood models. We investigate the asymptotic properties of these estimators both analytically and through simulation studies. We also apply a lasso estimator and compare its performance with the other proposed estimators.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".