Comparison of classical, kernel-based, and nearest neighbors regression estimators using the design-based Monte Carlo approach for two-phase forest inventories
Bibliographic record
Abstract
This paper compares design-based properties of the classical two-phase regression estimator with several nonparametric kernel-based estimators of which k nearest neighbors (kNN) is a special case. Metrics are based on the Euclidean distance applied to either a multidimensional space of explanatory variables or to a one-dimensional space of predictions obtained from a linear model. The main concepts of kernel-based regression estimators are reformulated in the design-based Monte Carlo approach to forest inventory. The results, based on a case study of a forest inventory in Switzerland and extensive simulations, suggest that the commonly used analytical external variance formula may systematically underestimate the true variance for a variety of kernel-based estimators including kNN but that it is still adequate for the classical regression estimator. Although using a bootstrap variance can help to correct this underestimation, it was also found that the bootstrap variance estimates could be unstable if the optimal bandwidth is recalculated in each bootstrap sample. These findings suggest that if the model captures the main features of the underlying process, then it is advisable to use the classical regression estimator, because it performs at least as well as the other techniques and is by far simpler to implement.
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".