Nonparametric Spatial Modeling towards the Mode
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Bibliographic record
Abstract
Existing models for spatial data analysis typically rely on mean or quantile regression to model the association between a dependent variable and covariates.We in this paper propose a novel spatial modal regression by assuming that the conditional mode of the response Y given covariates X follows a nonparametric regression structure, defined as m :and i Z N .The suggested spatial modal regression can be utilized to capture the "most likely" effect and may reveal new interesting data structures that are possibly missed by the conditional mean or quantiles, especially in cases of asymmetric data distributions.We derive the asymptotic distributions for the resulting modal estimators with appropriate choices of bandwidths.To numerically estimate the developed model, we recommend a modified modal expectation-maximization (MEM) algorithm with the assistance of a Gaussian kernel.Numerical examples are presented to demonstrate the favorable finite sample performance of the estimators.We also generalize the propounded spatial modal regression to an additive sum form to offer a versatile solution to handle high-dimensional datasets.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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