Asymptotic Normality of MLE

Set-up

Suppose we observe independent and identically distributed (i.i.d.) samples \(X_1,\ldots,X_n\) from a probability distribution \(p(\cdot; \theta)\) governed by a parameter \(\theta\) that is to be estimated. Then the likelihood for \(\theta\) is: \[L(\theta; X_1,\dots,X_n) := p(X_1,\ldots,X_n;\theta) = \prod_{i=1}^n p(X_i ; \theta).\] And the log-likelihood is: \[\ell(\theta; X_1,\dots,X_n):= \log L(\theta;X_1,\dots,X_n) = \sum_{i=1}^n \log p(X_i; \theta).\] Let \(\theta_0\) denote the true value of \(\theta\), and \(\hat{\theta}\) denote the maximum likelihood estimate (MLE). Because \(\ell\) is a monotonic function of \(L\) the MLE \(\hat{\theta}\) maximizes both \(L\) and \(\ell\). (In simple cases we typically find \(\hat{\theta}\) by differentiating the log-likelihood and solving \(\ell'(\theta;X_1,\dots,X_n)=0\).)

Result

Under some technical conditions that often hold in practice (often referred to as “regularity conditions”), and for \(n\) sufficiently large, we have the following approximate result: \[{\hat{\theta}} {\dot\sim} N(\theta_0,I_{n}(\theta_0)^{-1})\] where the precision (inverse variance), \(I_n(\theta_0)\), is a quantity known as the Fisher information, which is defined as \[I_{n}(\theta) := E_{\theta}\left[-\frac{d^2}{d\theta^2}\ell(\theta; X_1,\dots,X_n)\right].\] Notes:

• the notation \(\dot\sim\) means “approximately distributed as”.
• the expectation in the definition of \(I_n\) is with respect to the distribution of \(X_1,\dots,X_n\), \(p(X_1,\dots,X_n | \theta)\).
• The (negative) second derivative measures the curvature of a function, and so one can think of \(I_{n}(\theta)\) as measuring, on average, how curved the log-likelihood function is – or, in other words, how peaked the likelihood function is. The more peaked the likelihood function, the more information it contains, and the more precise the MLE will be.

With i.i.d. data the Fisher information can be easily shown to have the form \[I_n(\theta) = n I(\theta)\] where \(I(\theta)\) is the Fisher information for a {} observation - that is, \(I(\theta) = I_1(\theta)\). [This follows from the fact that \(\ell\) is the sum of \(n\) terms, and from linearity of expectation: Exercise!]

A slightly more precise statement

For those that dislike the vagueness of the statement “for \(n\) sufficiently large”, the result can be written more formally as a limiting result as \(n \rightarrow \infty\):

\[I_n(\theta_0)^{-0.5} (\hat{\theta} - \theta_0) \rightarrow N(0,1) \text{ as } n \rightarrow \infty\]

This kind of result, where sample size tends to infinity, is often referred to as an “asymptotic” result in statistics. So the result gives the “asymptotic sampling distribution of the MLE”.

While mathematically more precise, this way of writing the result is perhaps less intutive than the approximate statement above.

Sampling distribution

The interpretation of this result needs a little care. In particular, it is important to understand what it means to say that the MLE has a “distribution”, since for any given dataset \(X_1,\dots,X_n\) the MLE \(\hat{\theta}\) is just a number.

One way to think of this is to imagine sampling several data sets \(X_1,\dots,X_n\), rather than just one data set. Each data set would give us an MLE. Supppose we collect \(J\) datasets, and the \(j\)th dataset gives an MLE \(\hat{\theta}_j\). The distribution of the MLE means the distribution of these \(\hat{\theta}_j\) values. Essentially it tells us what a histogram of the \(\hat{\theta}_j\) values would look like. This distribution is often called the “sampling distribution” of the MLE to emphasise that it is the distribution one would get when sampling many different data sets. The concrete examples given below help illustrate this key idea.

Implication for estimation error

Notice that, because the mean of the sampling distribution of the MLE is the true value (\(\theta_0\)), the variance of the sampling distribution tells us how far we might expect the MLE to lie from the true value. Specifically the variance is, by definition, the expected squared distance of the MLE from the true value \(\theta_0\). Thus the standard deviation (square root of variance) gives the root mean squared error (RMSE) of the MLE.

For i.i.d. data the Fisher information \(I_n(\theta)=nI(\theta)\) and so increases linearly with \(n\) (see notes above). Consequently the variance decreases linearly with \(n\) and the RMSE decreases with \(n^0.5\). Thus, for example, to halve the RMSE we need to multiply sample size by 4.

This is a fundamental idea in statistics: for i.i.d. data (and under regularity conditions) estimation error in the MLE decreases as the square root of the sample size.