HIGHER ORDER ASYMPTOTICS: AN INTRINSIC DIFFERENCE BETWEEN UNIVARIATE AND MULTIVARIATE MODELS
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
summary Higher order asymptotic theory is targeted on the development of an asymptotic expansion for the distribution function of a statistic of interest. The asymptotic inference procedures are commonly based on simple characteristics of the density function at or near a data point of interest. In particular, exponential models are useful to provide accurate approximations to general statistical models. Typically, to the third order the exponential approximation has three primary parameters, two corresponding to pure model type and one for the departure from an exponential model (termed a non-exponentiality term). Andrews, Fraser and Wong (2005) discovered that to the third order, the observed significance function does not depend on the non-exponential term for univariate models. This finding has remarkable statistical implications for inference concerning univariate models. However, it is not clear whether this property holds for multivariate models. In this paper we address this question, and explore the intrinsic discrepancy between univariate and multivariate models.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it