A computational intelligent approach to estimate the Weibull parameters
Bibliographic record
Abstract
Fitting probability distributions, like Weibull distribution to data related to electronic components, is an essential activity in warranty forecasting model and lifetime analysing. This paper presents an evolutionary statistical approach (ESA), which yields both accurate and robust parameter estimates of lifetime distribution function for two parameters Weibull. Almost all estimation methods produce accurate results for the large sample size; however, more care must be taken in the selection of the estimation methods for extremely small sample size. It is known, for example, maximum likelihood estimation (MLE) estimates of the shape parameter for the Weibull distribution are biased for small sample sizes and the effect can be increased depending on the amount of censoring. In the Weibull distribution, the scale and shape parameters are calculated as an evaluation function by minimising the product of sum of squared errors (SSE) on both XY axes. Using SSE, the least squares estimation (LSE) and real-coded genetic algorithm methods, a simulation is carried out to compare the quality of these approaches. The results show that the ESA is superior to LSE and real-coded genetic algorithm methods, specifically, for a small sample size of data related to electronic components.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".