A Short Review on Bayesian Estimation of a Common Coefficient of Variation from Inverse Gaussian Distributions
Bibliographic record
Abstract
The coefficient of variation (CV) has been used in different disciplines with varied purpose related to variation in quantitative measurements. Statistical properties of CV have been studied by various researchers. Recently, a paper by the authors featuring an investigation of the posterior distribution of a common CV for inverse Gaussian populations with priors obtained through some empirical fitting procedure was presented by YPC at the 2022 ISBA World Meeting, June 26-July 1, 2022, held in Montreal, Canada. Some of these results along with other current developments by the authors on the topic were also reviewed during an invited presentation by MS in the honor of Dr. Daroga Singh at the 73rd Annual Conference of ISAS Conference held at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, November 14-16, 2022. The purpose of this paper is to summarize and discuss three key papers on estimation and testing of CV from inverse Gaussian distribution focussed on – a common CV (from multiple populations under frequentist framework), CV for a single population with Bayesian framework and a common CV from multiple populations and Bayesian framework, reviewed at this conference.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".