Question #280372

Let 𝑋1, … , 𝑋𝑛 be iid Gamma(1, 1/𝛼) RVs

(a) Show that the estimator T(𝑋1, … , 𝑋𝑛) = (n-1)/n𝑋̅ is the UMVUE for 𝛼 with 

variance 𝛼2/(𝑛 βˆ’ 2)

(b)Show that the minimum variance from CR inequality is 𝛼2/𝑛


1
Expert's answer
2021-12-17T10:40:49-0500

Solution:

(a):

Xi∼G(1,1/α)X_i\sim G(1,1/\alpha) are iid for all i=1,..,n

fxi(xi)=Ξ±βˆ’xiΞ±;xi>0f(Xβ€Ύ)=Ξ i=1nfxi(xi)=Ξ±neβˆ’Ξ£xiΞ±i=A(Ξ±)h(xβ€Ύ)eΞ·(Ξ±)T(xβ€Ύ)A(Ξ±)=Ξ±n,h(xβ€Ύ)=1,Ξ·(Ξ±)=βˆ’Ξ±,Tβ€²(xβ€Ύ)=Ξ£xif_{x_i}(x_i)=\alpha^{-x_i \alpha} ; x_i>0 \\ f( \underline{X})=\Pi_{i=1}^nf_{x_i}(x_i) \\=\alpha^ne^{-\Sigma x_i \alpha_i} \\=A(\alpha)h(\underline x)e^{\eta(\alpha)T(\underline x)} \\A(\alpha)=\alpha^n, h(\underline x)=1,\eta(\alpha)=-\alpha,T'(\underline x)=\Sigma x_i

∴f(Xβ€Ύ)\therefore f( \underline{X}) is from exponential family.

Now, Σxi∼G(n,1/α)\Sigma x_i\sim G(n,1/\alpha)

∴E(1Ξ£xi)=∫0∞1t.Ξ±ntnβˆ’1eβˆ’tΞ±Ξ“(n)dt=Ξ“(nβˆ’1)Ξ“(n)Ξ±=Ξ±nβˆ’1∴E(nβˆ’1nXΛ‰)=Ξ±\therefore E(\dfrac1{\Sigma x_i})=\int_0^{\infty}\dfrac 1t. \dfrac{\alpha^nt^{n-1}e^{-t \alpha}}{\Gamma(n)}dt \\=\dfrac{\Gamma(n-1)}{\Gamma(n)}\alpha \\=\dfrac{\alpha}{n-1} \\\therefore E(\dfrac{n-1}{n\bar X})=\alpha

So, T(Xβ€Ύ)=nβˆ’1nXΛ‰T(\underline X)=\dfrac{n-1}{n\bar X} is unbiased for Ξ±\alpha.

∴E(T(Xβ€Ύ)∣Tβ€²(Xβ€Ύ)=Ξ£xi)\therefore E(T(\underline X)|T'(\underline X)=\Sigma x_i) is UMVUE from Lehmann Scheffe theorem.

∴T(Xβ€Ύ)=nβˆ’1nXΛ‰\therefore T(\underline X)=\dfrac{n-1}{n\bar X} is UMVUE for Ξ±\alpha.

∴E(1(Ξ£xi)2)=∫0∞1t2.Ξ±ntnβˆ’1eβˆ’tΞ±Ξ“(n)dt=Ξ“(nβˆ’2)Ξ“(n)Ξ±2=Ξ±2(nβˆ’1)(nβˆ’2)\therefore E(\dfrac1{(\Sigma x_i)^2})=\int_0^{\infty}\dfrac 1{t^2}. \dfrac{\alpha^nt^{n-1}e^{-t \alpha}}{\Gamma(n)}dt \\=\dfrac{\Gamma(n-2)}{\Gamma(n)}\alpha^2 \\=\dfrac{\alpha^2}{(n-1)(n-2)}

V(nβˆ’1nXΛ‰)=(nβˆ’1)2V(1(Ξ£xi)2)=(nβˆ’1)2[E(1(Ξ£xi)2)βˆ’E2(1Ξ£xi)]=(nβˆ’1)2[Ξ±2(nβˆ’1)(nβˆ’2)βˆ’Ξ±2(nβˆ’1)2]=Ξ±2nβˆ’1V(\dfrac{n-1}{n\bar X})=(n-1)^2V(\dfrac1{(\Sigma x_i)^2}) \\=(n-1)^2[E(\dfrac1{(\Sigma x_i)^2})-E^2(\dfrac1{\Sigma x_i})] \\=(n-1)^2[\dfrac{\alpha^2}{(n-1)(n-2)} -\dfrac{\alpha^2}{(n-1)^2} ] \\=\dfrac{\alpha^2}{n-1}

(b):

fX(x)=Ξ±eβˆ’Ξ±x;x>0 & g(ΞΈ)=Ξ±β‡’gβ€²(ΞΈ)=1L(fX(x))=Ξ±neβˆ’Ξ±Ξ£xilog⁑L(fX(x))=nlogβ‘Ξ±βˆ’Ξ±Ξ£xiβˆ‚βˆ‚Ξ±log⁑L(fX(x))=nΞ±βˆ’Ξ£xiβˆ‚2βˆ‚Ξ±2log⁑L(fX(x))=βˆ’nΞ±2I(ΞΈ)=βˆ’E[βˆ‚2βˆ‚Ξ±2log⁑L(fX(x))]=βˆ’E[βˆ’nΞ±2]=nΞ±2f_X(x)=\alpha e^{-\alpha x};x>0\ \& \ g(\theta)=\alpha \Rightarrow g'(\theta)=1 \\L(f_X(x))=\alpha^n e^{-\alpha \Sigma x_i} \\ \log L(f_X(x))=n \log \alpha-\alpha \Sigma x_i \\ \dfrac{\partial }{\partial \alpha}\log L(f_X(x))=\dfrac{n}{\alpha}-\Sigma x_i \\ \dfrac{\partial^2 }{\partial \alpha^2}\log L(f_X(x))=\dfrac{-n}{\alpha^2} \\ I(\theta)=-E[\dfrac{\partial^2 }{\partial \alpha^2}\log L(f_X(x))] \\=-E[\dfrac{-n}{\alpha^2}]=\dfrac{n}{\alpha^2}

And CR inequality =CR=[gβ€²(ΞΈ)]2I(ΞΈ)=CR=\dfrac{[g'(\theta)]^2}{I(\theta)}

Here gβ€²(ΞΈ)=1g'(\theta)=1

So, CR=1nΞ±2=Ξ±2nCR=\dfrac{1}{\dfrac n{\alpha^2}}=\dfrac{\alpha^2}{n}

Hence, proved.


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