Past exam of the mathematics course of the University of Oxford / 2015 / Prelims / Mathematics III / 7

The central limit theorem states that if $W_1,W_2,\dots$ are independent and identically distributed random variables with finite mean $\mu$ and finite positive variance ${\sigma }^2$, thenHere the arrow means convergence in distribution: for every real $z$, the probability that the expression on the left is at most $z$ tends to $\Phi (z)$, the standard normal distribution function. If ${\sigma }^2=0$, the variables equal $\mu$ almost surely and their sample mean is exactly $\mu$.

Assuming $g$ is measurable, applying it separately to independent and identically distributed random variables preserves those properties. Thus $W_i=g(X_i)$ have common mean ${\mu }_g$ and variance ${\sigma }_g^2$. For ${\sigma }_g^2>0$, the central limit theorem givesHence, for large $n$, the approximate probability distribution isThe second argument is the variance. If ${\sigma }_g^2=0$, this sample mean equals ${\mu }_g$ almost surely, so the appropriate description is a point mass at ${\mu }_g$.

For an observed random sample with all $x_i>0$, which happens almost surely, independence gives the likelihood functionIgnoring a term independent of $\theta$, the log-likelihood and its derivative areThis derivative is positive for $0<\theta <S/n$ and negative for $\theta >S/n$. Thus there is a unique global maximum, and the maximum-likelihood estimator is

For $I_1$, the change of variables $t=x^2/\theta$ givesFor $k\ge 1$, use $d(e^{-x^2/\theta })=-(2x/\theta )e^{-x^2/\theta }dx$ and integration by parts:The boundary term vanishes: $x^{k+1}\to 0$ at zero, and the exponential decay dominates the power at infinity. These estimates also establish convergence of the improper integrals used above.

Be careful about the index: the probability density function already contains a factor $x$, so $\mathbb{E}[X_i^r]=I_{r+1}$. The recurrence relation at $k=1$ yields $I_3=\theta I_1=\theta$. Therefore linearity of expectation givesThus $\hat{\theta }$ is an unbiased estimator.

The recurrence relation at $k=3$ gives $I_5=2\theta I_3=2{\theta }^2$. ConsequentlyBy variance additivity for independent random variables, the variance of their sample mean is

Apply the central limit theorem to the independent and identically distributed random variables $X_i^2$, whose mean and variance are $\theta$ and ${\theta }^2$. It gives the approximate standard normal distributionThe supplied standard normal quantile $c=1.96$ leaves probability $0.025$ in each tail. HenceSince $\theta >0$, this event is equivalent toFor $n>1.96^2$, equivalently integer $n\ge 4$, both denominators below are positive. Inverting the inequalities gives the approximate $95\%$ confidence intervalThe approximation is justified for large $n$; positivity at $n\ge 4$ by itself does not guarantee accurate coverage. This construction is a confidence interval from a relative normal pivot: the unknown scale remains inside the pivotal ratio until the inequalities are inverted. Its meaning is that the random interval covers the fixed statistical parameter $\theta$ with probability approximately $0.95$ under repeated sampling.