Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918105259704320 |
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| author | Roy, Ujjwal Bhattacharya, Ritwik |
| author_facet | Roy, Ujjwal Bhattacharya, Ritwik |
| contents | The experimenter must perform a legitimate search in the entire set of feasible censoring schemes to identify the optimal type II progressive censoring scheme, when applied to a life-testing experiment. Current recommendations are limited to small sample sizes. Exhaustive search strategies are not practically feasible for large sample sizes. This paper proposes a meta-heuristic algorithm based on the genetic algorithm for large sample sizes. The algorithm is found to provide optimal or near-optimal solutions for small sample sizes and large sample sizes. Our suggested optimal criterion is based on the cost function and is scale-invariant for both location-scale and log-location-scale distribution families. To investigate how inaccurate parameter values or cost coefficients may affect the optimal solution, a sensitivity analysis is also taken into account. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach Roy, Ujjwal Bhattacharya, Ritwik Applications Statistics Theory Computation Methodology The experimenter must perform a legitimate search in the entire set of feasible censoring schemes to identify the optimal type II progressive censoring scheme, when applied to a life-testing experiment. Current recommendations are limited to small sample sizes. Exhaustive search strategies are not practically feasible for large sample sizes. This paper proposes a meta-heuristic algorithm based on the genetic algorithm for large sample sizes. The algorithm is found to provide optimal or near-optimal solutions for small sample sizes and large sample sizes. Our suggested optimal criterion is based on the cost function and is scale-invariant for both location-scale and log-location-scale distribution families. To investigate how inaccurate parameter values or cost coefficients may affect the optimal solution, a sensitivity analysis is also taken into account. |
| title | Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach |
| topic | Applications Statistics Theory Computation Methodology |
| url | https://arxiv.org/abs/2507.20001 |