Minimum-Distance Parametric Estimation Under Progressive Type-I Censoring
作者： Narayanaswamy Balakrishnan , Laurent Bordes , Xuejing Zhao
关键词: Normal distribution 、 Estimation theory 、 Censoring (clinical trials) 、 Parametric statistics 、 Applied mathematics 、 Asymptotic distribution 、 Mathematical optimization 、 Estimator 、 Nelson–Aalen estimator 、 Parametric model 、 Mathematics
摘要: The objective of this paper is to provide a new estimation method for parametric models under progressive Type-I censoring. First, we propose Kaplan-Meier nonparametric estimator the reliability function taken at censoring times. It based on observable number failures, and censored units occurring from scheme This then shown asymptotically follow normal distribution. Next, minimum-distance estimate unknown Euclidean parameter given model. leads consistent, estimators. maximum likelihood group-censored samples discussed next, efficiencies these two methods are compared numerically. Then, established results, derive obtain optimal scheme, Finally illustrate all results through Monte Carlo simulation study, an illustrative example.
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