CRAMER-RAO INEQUALITY
CRAMER-RAO INEQUALITY
Syllabus:
• Statement and proof of Cramer-Rao inequality
• Definition of minimum variance bound unbiased
estimator (MVBUE) of f().
• Proof of the following results .
• 1) If MVBUE exists for , then MVBUE exists
for f() , provided f is linear function.
• 2) If T is MVBUE for f() , then T is sufficient for
f() .
• If T is an unbiased estimator of satisfying
regularity condition, then T satisfies the
relation
CRAMER-RAO INEQUALITY
Syllabus ………..Continue
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• Examples and problems
Comparing estimators :
• Unbiased estimators can be compared via their variances and biased
estimators by comparing mean square errors. (Generally unbiased
estimators are preferable. )
• We can compare several unbiased estimators and find which one has
smallest variance, but this does not allow us to tell if an estimator has the
smallest variance amongst all unbiased estimators
• The Cramer-Rao lower bound provides a uniform lower bound
on the variance of all unbiased estimators of f = g( ).
• So if the variance of an unbiased estimator is equal to the
Cramer-Rao lower bound it must have minimum variance
amongst all unbiased estimators (so is said to be a minimum
variance unbiased estimator of f ).
CRAMER-RAO INEQUALITY : Statement
• Let X1, X2,….. Xn be a random sample from
f(x;), . Assume is a subset of the
real line. Let T=u(X1, X2,….. Xn ) be an
unbiased estimator of k(). Assume f(x;)
satisfies the regularity conditions, then
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Minimum Variance Bound Unbiased Estimator (MVBUE):
An unbiased estimator T of a parameter k() is called an MVBUE if Var(T)
attains Cramer-Rao lower bound.
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statistic for k()
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Definition 1 : Let T be an unbiased estimator of a parameter in
such a case of point estimation. The statistic T is called an efficient
estimator of if and only if the variance of T attains the Cramer-
Rao lower bound.
Definition 2: In cases in which we can differentiate with respect to
a parameter under an integral or summation symbol, the ratio of
the Cramer-Rao lower bound to the actual variance of any
unbiased estimator of a parameter is called the efficiency of that
statistic.
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