Nonparametric vs parametric binary choice models
Bibliographic record
Abstract
In France, despite access to safe public drinking water, and in spite of its excessively high price compared to tap water, 42 % of the population still regularly drink bottled water. Using scanner data on French consumption combined with raw water quality and other environmental data, and using a nonparametric kernel estimator of the conditional PDF, we show that poor raw water quality is an important factor driving the decision not to drink tap water. The estimated effect is found to be stronger for low-income households. Significant direct impacts of socioeconomic and demographic households' characteristics, as well as the role of cultural/regional factors are revealed. The aim of this paper is threefold. First, we employ a fully nonparametric model of a conditional PDF comprised of a binary response (choice) variable and continuous and discrete explanatory variables. Second, we address the issue of the performance of this nonparametric estimator relative to the parametric Probit specification which is dominant in applied settings and evaluate these estimators in a variety of ways. Third, we provide a detailed discussion of the results focusing on environmental insights provided by the two estimators, emphasizing how particular patterns detected using the nonparametric estimator are masked by the parametric specification.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".