BLUEs of Parameters of Generalized Geometric Distribution Using Ordered Ranked Set Sampling
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
ABSTRACT As an alternative to the best linear unbiased estimates based on order statistics (BLUE-OS) for general location-scale distributions given by Lloyd (1952 Lloyd , E. H. (1952). Least squares estimation of location and scale parameters using order statistics. Biometrika 39:88–95.[Crossref], [Web of Science ®] , [Google Scholar]) and Downton (1954 Downton , F. ( 1954 ). Least-squares estimates using ordered observations . Ann. Math. Statist. 25 : 303 – 316 .[Crossref] , [Google Scholar]), Bhoj and Ahsanullah (1996 Bhoj , D. S. , Ahsanullah , M. ( 1996 ). Estimation of parameters of the generalized geometric distribution using ranked set sampling . Biometrics 52 : 685 – 694 .[Crossref], [Web of Science ®] , [Google Scholar]) presented the best linear unbiased estimates based on ranked set sample (BLUE-RSS) for the generalized geometric distribution. Hossain and Muttlak (2000 Hossain , S. S. , Muttlak , H. A. ( 2000 ). Mvlue of population parameters based on ranked set sampling . Appl. Math. Computat. 108 : 167 – 176 . [CROSSREF] [Crossref], [Web of Science ®] , [Google Scholar]) extended it to some other distributions, and gave the BLUE-RSS for the population mean and the standard deviation. Bhoj and Ahsanullah (1996 Bhoj , D. S. , Ahsanullah , M. ( 1996 ). Estimation of parameters of the generalized geometric distribution using ranked set sampling . Biometrics 52 : 685 – 694 .[Crossref], [Web of Science ®] , [Google Scholar]) and Hossain and Muttlak (2000 Hossain , S. S. , Muttlak , H. A. ( 2000 ). Mvlue of population parameters based on ranked set sampling . Appl. Math. Computat. 108 : 167 – 176 . [CROSSREF] [Crossref], [Web of Science ®] , [Google Scholar]) arrived at the conclusion that the BLUE-RSS of the location parameter is more efficient than the BLUE-OS, while the BLUE-RSS of the scale parameter is not as efficient as the BLUE-OS for small n. In this article, we derive the best linear unbiased estimates using ordered ranked set sampling (BLUE-ORSS). These estimates are then compared with both BLUE-OS and BLUE-RSS for two special cases of the generalized geometric distribution. We show that BLUE-ORSS are uniformly better than BLUE-OS and BLUE-RSS not only for the location parameter but also for the scale parameter.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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