Editing Likelihood-ratio test (section)

===General===
Suppose that we have a [[statistical model]] with [[Statistical parameter|parameter space]] <math>\Theta</math>. A [[null hypothesis]] is often stated by saying that the parameter <math>\theta</math> lies in a specified subset <math>\Theta_0</math> of <math>\Theta</math>. The [[alternative hypothesis]] is thus that <math>\theta</math> lies in the [[Complement (set theory)|complement]] of <math>\Theta_0</math>, i.e. in <math>\Theta ~ \backslash ~ \Theta_0</math>, which is denoted by <math>\Theta_0^\text{c}</math>. The likelihood ratio test statistic for the null hypothesis <math>H_0 \, : \, \theta \in \Theta_0</math> is given by:<ref>{{cite book |first=Karl-Rudolf |last=Koch |author-link=Karl-Rudolf Koch |title=Parameter Estimation and Hypothesis Testing in Linear Models |url=https://archive.org/details/parameterestimat0000koch |url-access=registration |location=New York |publisher=Springer |year=1988 |isbn=0-387-18840-1 |page=[https://archive.org/details/parameterestimat0000koch/page/306 306]}}</ref>

:<math>\lambda_\text{LR} = -2 \ln \left[ \frac{~ \sup_{\theta \in \Theta_0} \mathcal{L}(\theta) ~}{~ \sup_{\theta \in \Theta} \mathcal{L}(\theta) ~} \right]</math>

where the quantity inside the brackets is called the likelihood ratio. Here, the <math>\sup</math> notation refers to the [[supremum]]. As all likelihoods are positive, and as the constrained maximum cannot exceed the unconstrained maximum, the likelihood ratio is [[Bounded set|bounded]] between zero and one.

Often the likelihood-ratio test statistic is expressed as a difference between the [[log-likelihood]]s
:<math>\lambda_\text{LR} = -2 \left[~ \ell( \theta_0 ) - \ell( \hat{\theta} ) ~\right]</math>
where 
: <math>\ell( \hat{\theta} ) \equiv \ln \left[~ \sup_{\theta \in \Theta} \mathcal{L}(\theta) ~\right]~</math>
is the logarithm of the maximized likelihood function <math>\mathcal{L}</math>, and <math>\ell(\theta_0)</math> is the maximal value in the special case that the null hypothesis is true (but not necessarily a value that maximizes <math>\mathcal{L}</math> for the sampled data) and
:<math> \theta_0 \in \Theta_0 \qquad \text{ and } \qquad \hat{\theta} \in \Theta~</math>
denote the respective [[arg max|arguments of the maxima]] and the allowed ranges they're embedded in. Multiplying by &minus;2 ensures mathematically that (by [[Wilks' theorem]]) <math>\lambda_\text{LR}</math> converges asymptotically to being [[chi-squared distribution|{{mvar|χ}}²-distributed]] if the null hypothesis happens to be true.<ref>{{cite book |first=S.D. |last=Silvey |title=Statistical Inference |location=London |publisher=Chapman & Hall |year=1970 |pages=112–114 |isbn=0-412-13820-4}}</ref> The [[Sampling distribution|finite-sample distribution]]s of likelihood-ratio statistics are generally unknown.<ref>{{cite book |first1=Ron C. |last1=Mittelhammer |author-link=Ron C. Mittelhammer |first2=George G. |last2=Judge |author-link2=George Judge |first3=Douglas J. |last3=Miller |title=Econometric Foundations |location=New York |publisher=Cambridge University Press |year=2000 |isbn=0-521-62394-4 |page=66}}</ref>

The likelihood-ratio test requires that the models be [[Statistical model#Nested models|nested]] &ndash; i.e. the more complex model can be transformed into the simpler model by imposing constraints on the former's parameters. Many common test statistics are tests for nested models and can be phrased as log-likelihood ratios or approximations thereof: e.g. the [[Z-test|''Z''-test]], the [[F-test|''F''-test]], the [[G-test|''G''-test]], and [[Pearson's chi-squared test]]; for an illustration with the [[Student's t-test#One-sample t-test|one-sample ''t''-test]], see below.

If the models are not nested, then instead of the likelihood-ratio test, there is a generalization of the test that can usually be used: for details, see ''[[relative likelihood]]''.