Likelihood-ratio test statistic

For nested null and alternative parameter spaces, the likelihood-ratio test statistic isUnder the regularity conditions of Wilks theorem, its null limit is a chi-squared distribution whose degrees of freedom equal the difference in model dimensions.

A Bartlett correction rescales a regular likelihood-ratio test statistic so that its null expected value matches its limiting chi-squared degrees of freedom to a higher asymptotic order. If ${\mathbb{E}}_{0}W=d(1+b/n+O(n^{-2}))$, divide $W$ by $1+b/n$. The operation uses a model-specific Bartlett correction coefficient; matching the mean alone is not a general proof of any claimed distributional error rate.

If ${\mathbb{E}}_{0}W=d(1+b/n+O(n^{-2}))$, the Bartlett-corrected statistic has mean $d+O(n^{-2})$. It is commonly calibrated against a chi-squared distribution with $d$ degrees of freedom. Equivalently, to the displayed order, one may multiply $W$ by $1-b/n$.

The coefficient $b$ is the leading relative null-mean discrepancy of a regular likelihood-ratio test statistic. It is distinct from the finite-sample divisor $1+b/n$ used in a Bartlett correction. Different conventions call either of these a correction factor, so the normalization should always be stated explicitly.

For a sample of size $n\ge 2$ from a normal distribution with unknown mean, the null variance likelihood-ratio test statistic is $W=n[-\log (U/n)+U/n-1]$, where $U\sim {\chi }_{n-1}^2$. With $V=(U-n)/n$, its expected Taylor terms start with $n[\mathbb{E}{V}^2/2-\mathbb{E}{V}^3/3+\mathbb{E}{V}^4/4]$. Here $\mathbb{E}{V}^2=(2n-1)/n^2$, $\mathbb{E}{V}^3=(2n-3)/n^3$, and $\mathbb{E}{V}^4=(12n^2+4n-15)/n^4$. Higher fixed-order terms contribute only $O(n^{-2})$ under valid termwise asymptotic integration. Thus ${\mathbb{E}}_{0}W=1+11/(6n)+O(n^{-2})$ and $W_B=W/[1+11/(6n)]$.