Recent Advances in Statistical Methods: Proceedings of Statistics 2001 Canada : The 4th Conference in Applied Statistics Montreal, Canada 6-8 July 2001
Bibliographic record
Abstract
Unemployment, Search and the Gender Wage Gap: A Structural Model (C Belzil & X Zhang) Kullback-Leibler Optimization of Density Estimates (A Berlinet & E Brunel) The Asymptotic Distribution of Spacings of Order Statistics (M G Bickis) Second-Order Moments and Mutual Information in the Analysis of Time Series (D R Brillinger) On the Robustness of Relative Surprise Inferences to the Choice of Prior (M Evans & T Zou) Using Survival Analysis in Pretern Birth Study (C Y Fu & S H Liu) A Nested Frailty Survival Model for Recurrent Events (R Ma et al.) Testing Goodness-of-Fit of the Gamma Models (C E Marchetti et al.) On Frailty Models and Copulas (D Oakes) Surrogate Data and Fractional Brownian Motion (P Rabinovitch) Nonlinear Mixed Effects Models: Recent Developments (P S R S Rao & N Zaino) Computational Sequence Analysis: Genomics and Statistical Controversies (P K Sen) Universal Optimality of Completely Randomized Designs (K R Shah & B K Sinha) Generalized Smoothed Estimating Functions with Censored Observations (A Thavaneswaran & J Singh) and other papers.
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.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.024 |
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".