Write and . The cumulant-generating function of one normal random variable is
Cramér's theorem states that empirical means of independent and identically distributed random variables whose moment-generating function is finite on a neighborhood of zero satisfy a large deviation principle at the sample-size large-deviation speed, with good rate function equal to the Legendre-Fenchel transform of their cumulant-generating function. Here that gives
For , differentiation gives the maximizing parameter , and hence
This rate function tends to infinity as , so its finite sublevel sets are compact. Equivalently, the Gärtner–Ellis theorem applies because the limiting scaled cumulant-generating function is finite and differentiable everywhere; it is lower semicontinuous and essentially smooth, with no finite domain boundary at which to check steepness. If is permitted, deterministically and the rate function is zero at and infinity elsewhere.

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