Maximum likelihood and maximum product of spacings estimations for the parameters of skew-normal distribution under doubly type II censoring using genetic algorithm
作者：
Highlights：
• ML and MPS estimates the parameters of Skew-normal under doubly type II censoring.
• GA is used to obtain the estimates instead of traditional numeric algorithms.
• Search space for GA is taken as data-driven confidence intervals of MML estimators.
• GA obtains ML and MPS estimates of three parameters simultaneously and effectively
• Simulation & Application results prove the superior performance of GA vs the others.
摘要
•ML and MPS estimates the parameters of Skew-normal under doubly type II censoring.•GA is used to obtain the estimates instead of traditional numeric algorithms.•Search space for GA is taken as data-driven confidence intervals of MML estimators.•GA obtains ML and MPS estimates of three parameters simultaneously and effectively•Simulation & Application results prove the superior performance of GA vs the others.
论文关键词：Skew-normal distribution,Genetic algorithm,Numerical methods,Maximum likelihood,Maximum product of spacings,Doubly type II censoring
论文评审过程：Received 29 June 2020, Revised 4 November 2020, Accepted 27 November 2020, Available online 13 December 2020, Version of Record 14 December 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114407
