Efficiency bounds for semiparametric models with singular score functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is concerned with asymptotic efficiency bounds for the estimation of the finite dimension parameter θ∈Rp of semiparametric models that have singular score function for θ at the true value θ⋆. The resulting singularity of the matrix of Fisher information means that the standard bound for θ−θ⋆ is not defined. We study the case of single rank deficiency of the score and focus on the case where the derivative of the root density in the direction of the last parameter component, θ2, is nil while the derivatives in the p – 1 other directions, θ1, are linearly independent. We then distinguish two cases: (i) The second derivative of the root density in the direction of θ2 and the first derivative in the direction of θ1 are linearly independent and (ii) The second derivative of the root density in the direction of θ2 is also nil but the third derivative in θ2 is linearly independent of the first derivative in the direction of θ1. We show that in both cases, efficiency bounds can be obtained for the estimation of κj(θ)=(θ1−θ⋆1,(θ2−θ⋆2)j), with j = 2 and 3, respectively and argue that an estimator θ̂ is efficient if κj(θ̂) reaches its bound. We provide the bounds in form of convolution and asymptotic minimax theorems. For case (i), we propose a transformation of the Gaussian variable that appears in our convolution theorem to account for the restricted set of values of κ2(θ). This transformation effectively gives the efficiency bound for the estimation of κ2(θ) in the model configuration (i). We apply these results to locally under-identified moment condition models and show that the generalized method of moments (GMM) estimator using V⋆−1 as weighting matrix, where V⋆ is the variance of the estimating function, is optimal even in these non standard settings. Examples of models are provided that fit the two configurations explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it