Local asymptotic normality
In statistics, local asymptotic normality is a property of a sequence of statistical models, which allows this sequence to be asymptotically approximated by a normal location model, after a rescaling of the parameter. An important example when the local asymptotic normality holds is in the case of iid sampling from a regular parametric model.
The notion of local asymptotic normality was introduced by.Definition
A sequence of parametric statistical models is said to be locally asymptotically normal at θ if there exist matrices rn and Iθ and a random vector such that, for every converging sequence,
where the derivative here is a Radon–Nikodym derivative, which is a formalised version of the likelihood ratio, and where o is a type of big O in probability notation. In other words, the local likelihood ratio must converge in distribution to a normal random variable whose mean is equal to minus one half the variance:
The sequences of distributions and are contiguous.Example
The most straightforward example of a LAN model is an iid model whose likelihood is twice continuously differentiable. Suppose is an iid sample, where each Xi has density function. The likelihood function of the model is equal to
If f is twice continuously differentiable in θ, then
Plugging in, gives
By the central limit theorem, the first term converges in distribution to a normal random variable, whereas by the law of large numbers the expression in second parentheses converges in probability to Iθ, which is the Fisher information matrix:
Thus, the definition of the local asymptotic normality is satisfied, and we have confirmed that the parametric model with iid observations and twice continuously differentiable likelihood has the LAN property.
