Likelihood-ratio test

A likelihood-ratio test rejects where the alternative likelihood is sufficiently large relative to the null likelihood.

For a scalar parameter restricted to $\psi \ge 0$, test $\psi =0$ against $\psi >0$ with identifiable interior nuisance parameters. A locally quadratic regular likelihood has an efficient null score $Z\sim N(0,1)$, and the constrained fit projects the unconstrained local maximizer onto the nonnegative half-line. ConsequentlyFor a positive statistic, its asymptotic upper-tail p-value is half the ordinary one-degree chi-squared tail. Its 5% critical value is the 90th chi-squared percentile, approximately 2.7055. Additional boundary nuisance parameters or nonidentified mixture parameters can change this law.

Testing a single nonnegative random-effect variance against zero puts the null on the boundary of the parameter space. In the usual regular increasing-independent-groups limit, the likelihood-ratio test statistic has limit $\frac{1}{2}{\delta }_0+\frac{1}{2}{\chi }_1^2$, rather than ${\chi }_1^2$. A finite-sample simulation calibrated for the actual design avoids relying on this asymptotic approximation.

With fixed $X,Z$, compare $N(X\beta ,{\sigma }^{2}I)$ and $N(X\beta ,{\sigma }^{2}I+{\tau }^{2}Z{Z}^T)$ by maximizing the ordinary likelihood function in both models. Their likelihood-ratio test statistic is unchanged by $Y\mapsto Xb+cY$, $c>0$, because both maximized log-likelihoods change by the same $-n\log c$. Its null law can therefore be simulated using independent $N(0,I)$ responses. Comparing the observed statistic with simulated statistics by $(1+\#\{T_b\ge T_{obs}\})/(B+1)$ gives a conservative finite-sample Monte Carlo p-value, subject to correctly maximizing both likelihoods.

For two parameter values ${\theta }_1$ and ${\theta }_0$, the likelihood ratio is $f(x\mid {\theta }_1)/f(x\mid {\theta }_0)$.

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 generalized likelihood-ratio test compares the likelihood maximized under the null with the likelihood maximized over the full parameter space.

Two equally numerous groups of independent counts with Poisson distributions have areas $a$ and $2a$. Under common density their group means are in ratio $1:2$. With group sums $S_1,S_2$ and $T=S_1+S_2$, the maximized alternative-to-null likelihood ratio isUse zero-count limits and $\Lambda =1$ when $T=0$. The regular Wilks theorem calibration has one degree of freedom. Exactly conditioning on $T$ gives $S_1\sim \mathrm{Binomial}(T,1/3)$ under the null; this supports small-count testing without the large-sample approximation.

The deviance of a fitted generalized linear model isFor regular nested models, the deviance reduction $D_0-D_1=2({\ell }_1-{\ell }_0)$ is asymptotically chi-squared with degrees of freedom equal to the difference in fitted dimensions.

The unscaled residual deviance of an exponential dispersion family compares its fitted mean with the saturated statistical model at a common dispersion parameter. Its normalization removes the factor $1/\varphi$ in the log-likelihood. In Poisson regression and binomial regression, $\varphi =1$, so it is twice the saturated-minus-fitted log-likelihood. Under suitable regularity, $D/\varphi$ has approximately a chi-squared distribution with the residual degrees of freedom; this is not an automatic accurate approximation for sparse responses. Nested deviance reductions with estimated dispersion lead to approximate F-tests after scaling by the dispersion estimate.

For independent unit-variance samples, testing equal means uses $Z=\sqrt{mn/(m+n)}(\bar{X}-\bar{Y})$ and rejects for large $|Z|$.

Likelihood-ratio tests of different null subspaces can have crossing rejection regions; comparing their quadratic-form geometry exhibits observations accepted by one and rejected by another.