PARAMETER ESTIMATION IN A PARTLY LINEAR REGRESSION MODEL WITH RANDOM COEFFICIENT AUTOREGRESSIVE ERRORS
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Bibliographic record
Abstract
ABSTRACT Consider a partly linear regression model where Yi 's are responses, and are fixed design points, is an unknown parameter vector, is an unknown bounded real-valued function defined on a compact subset of the real line , and are unobservable random errors. We study the above model when is a first-order random coefficient autoregressive process, i.e., a stationary solution of , where {zi } and {ei } are zero mean independent processes each consisting of i.i.d. random variables with finite second moments and respectively. Various estimators of β, θ and are investigated and their limit distributions established. Consistent estimators of the covariance matrices of the various estimators of β are also proposed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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