Statistical inference in Lombard's smooth‐change model
Bibliographic record
Abstract
Abstract The sample properties of various inference procedures in Lombard's smooth‐change model are studied in this work. In particular, the power of six test statistics for the detection of change‐points in the mean and the variance of a series of independent observations is investigated under several alternatives. The robustness of the procedures under heterogeneity and serial dependence is considered as well. An investigation of the efficiency of an estimator of the change‐points is also presented. Conditional on these estimated change‐points, least squares estimators of the means in Lombard's model are derived and their efficiency is carefully studied. The procedures are illustrated on two environmental data sets, namely the annual volume of discharge from the Nile River and the annual temperature anomalies for the northern hemisphere. It will be seen that Lombard's model is flexible, that the test statistics of Lombard (1987) are powerful, and that the proposed estimators have nice properties; hence Lombard's model has a high potential for applications in the environmental sciences. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.048 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".