Rating Area-yield Crop Insurance Contracts Using Bayesian Model Averaging and Mixture Models
Bibliographic record
Abstract
Given increasing interest in area-yield crop insurance, many methods to estimate crop yield densities have been presented in the literature. Most of these methods are of parametric form, such as Normal, Beta, or Normal mixture. All previous research choose what they believe to be the single best method, which necessarily fails to take into account model uncertainty. This is problematic because any given model is only true with some level of probability less than one. Inference based on this single model methodology may lead to biased and inaccurate estimation results. An estimation method rooted in the Bayesian paradigm is proposed in this thesis. The proposed method, employing Bayesian Model Averaging and mixture models, takes into account model uncertainty and shows strong performance in simulations. In addition, this thesis considers a roughness penalty for the BIC in the Bayesian model averaging. The methodology is applied to rating area-yield crop insurance contracts for corn and soybean in Iowa, U.S.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".