110 likes | 330 Views
7.4 PROPERTIES OF ESTIMATORS. 1 properties. Unbiased estimation. is a nonrandom parameter is a random parameter. For N observation data:. Commonly, quantities of observation data are beneficial to properties of estimators. Biased estimation:. Neyman Fisher factor theory.
E N D
7.4 PROPERTIES OF ESTIMATORS 1 properties Unbiased estimation is a nonrandom parameter is a random parameter For N observation data: Commonly, quantities of observation data are beneficial to properties of estimators. Biased estimation:
Neyman Fisher factor theory effectiveness To unbiased estimation, variance get to least consistent sufficient
Var of unbiased estimation: 2 Cramer-Rao Low Bound (1) nonrandom parameter CRLB
In the condition of: An unbiased estimation,if variance can get to CRLB,is ML estimation,and it’s a best estimation.
Because if It is certain for ML estimation of follow two expressions: so
(2) CRLB of random parameter MSE of unbiased estimation: If MSE get to the least need:
An unbiased estimation,if variance can get to CRLB,is MAP estimation, and MS equal to ML in this condition. example: