MVU estimator does not always exist as \u03b8 must have smallest variance for all

# Mvu estimator does not always exist as θ must have

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MVU estimator does not always exist, as θ must have smallest variance for all values of θ . 1 1 To emphasize the fact that the MVU estimator must have the smallest variance for all values of θ , B & D refer to it as uniformly minimum variance unbiased (UMVU). EE 527, Detection and Estimation Theory, # 1 12 Comments: Even if it exists for a particular problem, MVU estimator is not optimal in terms of minimizing the MSE and we may be able to do better. Unbiasedness is nice, but not the most important = we can relax this condition and consider biased estimators as well, e.g. by making them asymptotically unbiased . By relaxing the unbiasedness condition, it is possible to outperform the MVU estimators in terms of MSE, as shown in the following example. What we really care about is minimizing the MSE! Example 2 2 . Consider now estimating the variance σ 2 of independent, identically distributed (i.i.d.) zero-mean Gaussian observations, using the following estimator: σ 2 = a · 1 N N - 1 n =0 x 2 [ n ] (1) where a > 0 is variable. If we choose a = 1 , σ 2 a =1 will be unbiased 3 with σ 2 a =1 = σ 2 MVU = 1 N N - 1 n =0 x 2 [ n ] . (2) 2 See also P. Stoica and R. Moses, “On biased estimators and the unbiased Cram´ er-Rao lower bound,” Signal Processing, vol. 21, pp. 349–350, 1991. 3 We will show later that this choice yields an MVU estimate. EE 527, Detection and Estimation Theory, # 1 13 Now, in general, E [ σ 2 ] = a σ 2 and MSE( σ 2 ) = E [( σ 2 - σ 2 ) 2 ] = E [ σ 4 ] + σ 4 - 2 σ 2 E [ σ 2 ] = E [ σ 4 ] + σ 4 (1 - 2 a ) = a 2 N 2 N - 1 n 1 =0 N - 1 n 2 =0 E { x 2 [ n 1 ] x 2 [ n 2 ] } + σ 4 (1 - 2 a ) = a 2 N 2 [( N 2 - N ) σ 4 + N · E { x 4 [ n ] } 3 σ 4 ) + σ 4 (1 - 2 a ) = σ 4 · a 2 (1 + 2 N ) + (1 - 2 a ) . (3) To evaluate the above expression, we have used the following facts: For n 1 = n 2 , E { x 2 [ n 1 ] x 2 [ n 2 ] } = E { x 2 [ n 1 ] } · E { x 2 [ n 2 ] } = σ 2 · σ 2 = σ 4 . For n 1 = n 2 , E { x 2 [ n 1 ] x 2 [ n 2 ] } = E { x 4 [ n 1 ] } = 3 σ 4 (which is the fourth-order moment of a Gaussian distribution). EE 527, Detection and Estimation Theory, # 1 14 It can be easily shown that ( 3 ) is minimized for a OPT = N N + 2 yielding the estimator σ 2 = a OPT · 1 N N - 1 n =0 x 2 [ n ] whose MSE MSE MIN = 2 σ 4 N + 2 . is minimum for the family of estimators in ( 1 ). Comments: σ 2 is biased and has smaller MSE than the MVU estimator in ( 2 ): MSE MIN < MSE( σ 2 ) a =1 = 2 σ 4 N . Note that we are able to construct an realizable estimator in this case — compare with Example 1 in this handout. For large N , σ 2 and σ 2 MVU are approximately the same since N/ ( N + 2) 1 as N → ∞ . This also implies that σ 2 is asymptotically unbiased . EE 527, Detection and Estimation Theory, # 1 15 Note: I do not wish to completely dismiss bias considerations. For example, we may have two estimators θ 1 and θ 2 with [bias( θ 1 )] 2 var( θ 1 ) and [bias( θ 2 )] 2 var( θ 2 ) and MSE( θ 1 ) MSE( θ 2 ) . So, these two estimators are “equally good” as far as MSE is concerned. But, we may have | bias( θ 1 ) | | bias( θ 2 ) | making θ 1 “more desirable” than θ 2 . Bias correction methods have been developed for constructing estimators that have small bias. Hence, having small bias is typically a second-tier  #### You've reached the end of your free preview.

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