Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.
Bibliographic record
Abstract
The first two articles build procedures to simulate vector of univariate states\nand estimate parameters in nonlinear and non Gaussian state space models. We\npropose state space speci fications that offer more flexibility in modeling dynamic\nrelationship with latent variables. Our procedures are extension of the HESSIAN\nmethod of McCausland[2012]. Thus, they use approximation of the posterior density\nof the vector of states that allow to : simulate directly from the state vector\nposterior distribution, to simulate the states vector in one bloc and jointly with the\nvector of parameters, and to not allow data augmentation. These properties allow\nto build posterior simulators with very high relative numerical efficiency. Generic,\nthey open a new path in nonlinear and non Gaussian state space analysis with\nlimited contribution of the modeler.\nThe third article is an essay in commodity market analysis. Private firms coexist\nwith farmers' cooperatives in commodity markets in subsaharan african countries.\nThe private firms have the biggest market share while some theoretical models predict\nthey disappearance once confronted to farmers cooperatives. Elsewhere, some\nempirical studies and observations link cooperative incidence in a region with interpersonal trust, and thus to farmers trust toward cooperatives. We propose a model\nthat sustain these empirical facts. A model where the cooperative reputation is a\nleading factor determining the market equilibrium of a price competition between\na cooperative and a private firm
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 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".