Paper 2, Section II,

Principles of Statistics | Part II, 2016

(a) State and prove the Cramér-Rao inequality in a parametric model {f(θ):θ∈Θ}\{f(\theta): \theta \in \Theta\}, where Θ⊆R\Theta \subseteq \mathbb{R}. [Necessary regularity conditions on the model need not be specified.]

(b) Let X1,…,XnX_{1}, \ldots, X_{n} be i.i.d. Poisson random variables with unknown parameter EX1=θ>0E X_{1}=\theta>0. For Xˉn=(1/n)∑i=1nXi\bar{X}_{n}=(1 / n) \sum_{i=1}^{n} X_{i} and S2=(n−1)−1∑i=1n(Xi−Xˉn)2S^{2}=(n-1)^{-1} \sum_{i=1}^{n}\left(X_{i}-\bar{X}_{n}\right)^{2} define

Tα=αXˉn+(1−α)S2,0⩽α⩽1T_{\alpha}=\alpha \bar{X}_{n}+(1-\alpha) S^{2}, \quad 0 \leqslant \alpha \leqslant 1

Show that Var⁡θ(Tα)⩾Var⁡θ(Xˉn)\operatorname{Var}_{\theta}\left(T_{\alpha}\right) \geqslant \operatorname{Var}_{\theta}\left(\bar{X}_{n}\right) for all values of α,θ\alpha, \theta.

Now suppose θ~=θ~(X1,…,Xn)\tilde{\theta}=\tilde{\theta}\left(X_{1}, \ldots, X_{n}\right) is an estimator of θ\theta with possibly nonzero bias B(θ)=Eθθ~−θB(\theta)=E_{\theta} \tilde{\theta}-\theta. Suppose the function BB is monotone increasing on (0,∞)(0, \infty). Prove that the mean-squared errors satisfy

Eθ(θ~n−θ)2⩾Eθ(Xˉn−θ)2 for all θ∈ΘE_{\theta}\left(\tilde{\theta}_{n}-\theta\right)^{2} \geqslant E_{\theta}\left(\bar{X}_{n}-\theta\right)^{2} \text { for all } \theta \in \Theta

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