Kernel mode-based varying coefficient models with nonstationary regressors
Why this work is in the frame
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Bibliographic record
Abstract
We propose estimating varying coefficient models based on the mode value using a kernel objective function, allowing for both stationary and unit root regressors. This kernel mode-based estimation is more robust and efficient than least squares estimation for data with outliers or heavy-tailed distributions, without sacrificing efficiency when the data follow a normal distribution. We develop a local linear approximation scheme to estimate the varying coefficient function. We show that the nonparametric estimator of the varying coefficient function with nonstationary regressors converges faster than the estimator with stationary regressors. To achieve estimation optimality, we further suggest a kernel mode-based two-step estimation procedure for estimating the stationary component. For numerically solving the model, we recommend a mode expectation-maximization algorithm and introduce a data-driven method for choosing the optimal bandwidths. We illustrate the finite sample performance of the developed estimators through Monte Carlo simulations and a real data application.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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