Applied Nonparametric Regression Analysis: The Choice of Generalized Additive Models
Bibliographic record
Abstract
Literature has documented tremendous changes in classical regression analysis techniques since 1980s. The drawbacks of simple and multiple parametric regression analyses on model specifications and the non-robust assumption of error terms followed by the introduction of a series of diagnostic tests to fix these inevitable pitfalls have made econometricians to develop new methodologies in nonparametric and semi-parametric regressions that either do not have or mitigate the major shortcomings of what their traditional counterparts inherently demonstrate. The development of the generalized linear models followed by the introduction of generalized additive models and generalized additive mixed models has attracted practitioners to use these methodologies in applied studies. The main objective of this paper is to conduct a comprehensive survey on studies that used generalized additive models as econometric models and show how the parameters of these models are estimated. In particular, it briefly reviews the theory of generalized additive models, and then introduces various techniques to estimate the parameters of the models. Finally, it presents a comprehensive review of studies in which generalized additive models are specified as the econometric model.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".