Parametric Modal Regression with Autocorrelated Error Process
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Bibliographic record
Abstract
We propose an efficient two-step estimation procedure for a parametric modal regression with autoregressive errors.The procedure relies on estimating a parametric transformation of the dependent variable from data using a (penalized) kernel-based objective function.We establish asymptotic normality for the resulting estimator and demonstrate that it possesses oracle properties, as if the true order of autoregressive error structure were known in advance.To numerically estimate modal parameter and determine the order of error structure, two modified (penalized) modal expectation-maximization (MEM) algorithms are developed.Furthermore, we present a modal residual-based autocorrelation test and show that the statistic is asymptotically distributed as a X 2 distribution.Monte Carlo simulations and an empirical analysis are conducted to illustrate the finite sample performance of the resultant estimator.We also discuss the extension of the results to a nonparametric modal regression model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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