[RP]Lecture Note VIII - Kyung Hee University Department of...

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Kyung Hee University Random Processing Department of Electronics and Radio Engineering Prof. Hyundong Shin Communications and Coding Theory Laboratory (CCTLAB) VIII-1 C1002900 RP Lecture Handout VIII: Parameter Estimation Reading: Chapter 8.1-8.3 In a statistical investigation, there are two general classes of problems: (i) The probabilistic model is known, we wish to make predictions concerning fu- ture observations.  ; X F x x x For example, we know the distribution X F x of a random variable X and we wish to predict the average x of its n future samples. (ii) One or more parameters i of the model are unknown and we wish either to estimate their values (parameter estimation) or to decide whether i is a set of known constant i 0 (hypothesis testing). ; X F x i x ˆ ˆ For example, we observe the values i x of X and wish to estimate its mean X (nonrandom but unknown) or to decide whether to accept the hypothesis that X  5 . Let   T n XX X X 12 denote n random variables representing observations x   T n xx x .
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Kyung Hee University Random Processing Department of Electronics and Radio Engineering Prof. Hyundong Shin Communications and Coding Theory Laboratory (CCTLAB) VIII-2 We are interested in estimating an unknown nonrandom parameter based on this data. The joint pdf  ; f X x of X depends on . We form an estimate for the unknown parameter as function of these ob- served samples ˆ g  x . (VIII.1) valid form of g x : not function of Estimator: ˆ : statistic g  X (VIII.2) 8.1. Bias and Error Variance Estimator error:   ˆ e   XX . (VIII.3) The bias in an estimator ˆ is defined as       ˆ ˆ ˆ . be X X X (VIII.4) The error variance:       ˆˆ . eb       2 2 X (VIII.5) ˆ b and ˆ  2 are in general functions of the parameter . We say an estimator ˆ for a nonrandom parameter is unbiased if ˆ b 0 for all possible values of :   ˆ X . If ˆ X 1 and ˆ X 2 are both unbiased estimators for , it is obvious that we want the one with lower variance.
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Kyung Hee University Random Processing Department of Electronics and Radio Engineering Prof. Hyundong Shin Communications and Coding Theory Laboratory (CCTLAB) VIII-3 How does one find an unbiased estimator for with the minimum possible va- riance? Is it possible to express the lower bound to the variance of all unbiased estimators? 8.2. Minimum-Variance Unbiased (MVU) Estimator Let denote the set of all estimators that are unbiased:    ˆ ˆˆ is valid such that . b   0 (VIII.6) Then, when it exists, the MVU estimator for is defined as   ˆ MVU ˆ ˆ argmin   2 ,  . (VIII.7) MVU ˆ may not exist. For some problems, the set is empty, there are no un- biased estimators. In other case,
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[RP]Lecture Note VIII - Kyung Hee University Department of...

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