 Coverage failure under random variance-ratio limits

Coverage failure under random variance-ratio limits can persist even when the centre is exactly normal, the variance estimate is conservative in expectation, and its absolute error tends to zero. What fails is relative-scale concentration. Let $Z\sim N(0,1)$ and a positive random scale $R$ be independent, and defineThe variance ratio is $R$ at every sample size. Its nondegenerate distribution need not approach the constant required in asymptotic conservative Wald coverage under variance-ratio convergence.

Conditional on $R$, a normal critical value $z>0$ gives interval coverage $f(R)=2\Phi (z\sqrt{R})-1$. Therefore unconditional coverage is $Ef(R)$ under the independence assumption. A useful shape calculation isThe function is strictly concave. If $ER=1$, Jensen's inequality puts $Ef(R)\le f(1)$, with strict inequality for a nonconstant scale. An expectation equality therefore does not reproduce the normal interval's nominal coverage.

For an original strictly conservative example, let $R$ take $0.25$ and $2.75$ with equal probabilities. Then $E{\hat{V}}_n=1.5V_n$, but at nominal 95 percent the exact coverage isThis value is unchanged as $n$ grows. Meanwhile ${\hat{V}}_n-V_n=(R-1)/n$ tends to zero, showing why absolute consistency at a vanishing variance scale is insufficient. The normal cdf evaluation uses the pinned primary numerical definition.^[ref]

If the scale depends on the standardized error, the conditional normal formula above cannot simply be retained; the joint law is needed. NIST likewise distinguishes a justified coverage probability from selecting a conventional uncertainty multiplier.^[ref]