{"ID":2890212,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20001","arxiv_id":"2507.20001","title":"Computation of Optimal Type-II Progressing Censoring Scheme Using Genetic Algorithm Approach","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.20001","url_pdf":"https://arxiv.org/pdf/2507.20001v1","authors":"[\"Ujjwal Roy\",\"Ritwik Bhattacharya\"]","published":"2025-07-26T16:40:20Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"math.ST\",\"stat.CO\",\"stat.ME\"]","methods":"[]","has_code":false}