In this paper, we have established a new framework of multistage parametric estimation. Specially, we have developed sampling schemes for estimating parameters of common important distributions. Without any information of the unknown parameters, our sampling schemes rigorously guarantee prescribed levels of precision and confidence, while achieving unprecedented efficiency in the sense that the average sampling numbers are virtually the same as that are computed as if the exact values of unknown parameters were available.
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