Let 𝑋1, … , 𝑋𝑛 be a random sample from the Bernoulli distribution,
say P[X=1] = θ=1-P[X=0]. (a) Find the CRLB for the variance of unbiased estimators of
θ(1-θ). (b) Find the UMVUE of θ(1-θ) if such exists.
"a)"
For a Bernoulli random variable,
"f(x,\\theta)=\\theta^x(1-\\theta)^{1-x},\\space x=0,1"
To find the CRLB, we proceed as follows.
"lnf(x,\\theta)=ln(\\theta^x(1-\\theta)^{1-x})=xln\\theta+(1-x)ln(1-\\theta)"
"{\\delta\\over \\delta\\theta}(lnf(x,\\theta))={x\\over\\theta}-{(1-x)\\over (1-\\theta)}"
"E({\\delta\\over \\delta\\theta}(lnf(x,\\theta)))^2=E({x\\over\\theta}-{(1-x)\\over (1-\\theta)})^2={1\\over(\\theta(1-\\theta))^2}E(x-\\theta)^2={1\\over\\theta(1-\\theta)}"
Now,
"\\Tau(\\theta)=\\theta(1-\\theta)"and "\\Tau'(\\theta)=1-2\\theta"
Therefore,
"var(\\Tau(\\theta))=var(\\theta(1-\\theta))={(\\Tau'(\\theta))^2\\over n\\times E({\\delta\\over \\delta\\theta}(lnf(x,\\theta)))^2 }={(1-2\\theta)^2\\over n\\times{1\\over(\\theta(1-\\theta))}}={(1-2\\theta)^2\\times \\theta(1-\\theta)\\over n}={\\theta\\over n}(1-5\\theta+8\\theta^2-4\\theta^3)"
Thus, the CRLB is given by,
"var(\\theta(1-\\theta))\\ge{\\theta\\over n}(1-5\\theta+8\\theta^2-4\\theta^3)"
"b)"
Let "T=X_1+X_2+...+X_n". Therefore, "T=X_1+X_2+...+X_n" is a complete sufficient statistic . By the Lehmann-Scheffe theorem, if we can find a function of "T" whose expectation is "\\theta(1-\\theta)", it is an UMVUE.
In any set up, the sample variance "{1\\over(n-1)}\\sum(X_i-\\bar{X})^2", is an unbiased estimate of the variance. Since "X^2=X" for Bernoulli random variables,
"{1\\over(n-1)}\\sum(X_i-\\bar{X})^2={1\\over(n-1)}(\\sum X_i^2-n\\bar{X}^2)"
"={1\\over(n-1)}(\\sum X_i-n\\bar{X}^2)"
"={T(n-T)\\over n(n-1)}"
Hence, "{T(n-T)\\over n(n-1)}" is an UMVUE for the variance, "\\theta(1-\\theta)"
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