A Direct Iterative Technique for Weibull Parameters Estimation Optimized for lowest Root Mean Square Error
Bibliographic record
Abstract
Classical Weibull distribution (Weibull minima) is gaining momentum due to its employment in wind speed estimation for the growing renewable energy field. The main difficulty of Weibull distribution is that it is hard to accurately estimate its parameters for a set of wind speed data. Much research has been involved in this task with varying complexity, success, and accuracy. Statistical analysis is usually used to compare the accuracy of proposed Weibull parameters estimation methods. As the Weibull parameters of a set of data are not normally estimated, for simulation, more than once, this research considered the processing time to be of far less importance than accuracy. In this research, efforts were made to make the best use of the current PC processing power to achieve the best possible accuracy of the Weibull parameters. In the current research, an iterative technique is proposed to directly estimate the Weibull parameters that yield the best results with the most employed statistical analysis; least Root Mean Square Errors of estimation (RMSE). The results of the new technique were compared to that of the Maximum likelihood method (MLM), and a considerable improvement in accuracy, 9.3 % was achieved.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".