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What is Cramer-Rao Inequality in Estimation about?

This document discusses efficiency and the Cramer-Rao lower bound for parameter estimation. It begins by defining Fisher information and showing that it provides a lower bound for the variance of an unbiased estimator. Specifically, the Cramer-Rao inequality states that the variance of an unbiased estimator must be greater than or equal to the inverse of the sample size times the Fisher information. Two examples are provided to illustrate calculating Fisher information for specific distributions. The docume

Author

hoithanh

Language

EN