# Score test

Rao's score test, or the score test (often known as the Lagrange multiplier test in econometrics) is a statistical test of a simple null hypothesis that a parameter of interest $\theta$ is equal to some particular value $\theta _{0}$ . It is the most powerful test when the true value of $\theta$ is close to $\theta _{0}$ . The main advantage of the Score-test is that it does not require an estimate of the information under the alternative hypothesis or unconstrained maximum likelihood. This makes testing feasible when the unconstrained maximum likelihood estimate is a boundary point in the parameter space.

## Single parameter test

### The statistic

$U(\theta )={\frac {\partial \log L(\theta |x)}{\partial \theta }}.$ ${\mathcal {I}}(\theta )=-\operatorname {E} \left[\left.{\frac {\partial ^{2}}{\partial \theta ^{2}}}\log L(X;\theta )\right|\theta \right]\,.$ #### Note on notation

Note that some texts use an alternative notation, in which the statistic $S^{*}(\theta )={\sqrt {S(\theta )}}$ is tested against a normal distribution. This approach is equivalent and gives identical results.

### As most powerful test for small deviations

$\left({\frac {\partial \log L(\theta |x)}{\partial \theta }}\right)_{\theta =\theta _{0}}\geq C$ The score test is the most powerful test for small deviations from $H_{0}$ . To see this, consider testing $\theta =\theta _{0}$ versus $\theta =\theta _{0}+h$ . By the Neyman-Pearson lemma, the most powerful test has the form

${\frac {L(\theta _{0}+h|x)}{L(\theta _{0}|x)}}\geq K;$ Taking the log of both sides yields

$\log L(\theta _{0}+h|x)-\log L(\theta _{0}|x)\geq \log K.$ The score test follows making the substitution (by Taylor series expansion)

$\log L(\theta _{0}+h|x)\approx \log L(\theta _{0}|x)+h\times \left({\frac {\partial \log L(\theta |x)}{\partial \theta }}\right)_{\theta =\theta _{0}}$ ### Relationship with other hypothesis tests

The likelihood ratio test, the Wald test, and the Score test are asymptotically equivalent tests of hypotheses. When testing nested models, the statistics for each test converge to a Chi-squared distribution with degrees of freedom equal to the difference in degrees of freedom in the two models.

## Multiple parameters

A more general score test can be derived when there is more than one parameter. Suppose that ${\hat {\theta }}_{0}$ is the maximum likelihood estimate of $\theta$ under the null hypothesis $H_{0}$ . Then

$U^{T}({\hat {\theta }}_{0})I^{-1}({\hat {\theta }}_{0})U({\hat {\theta }}_{0})\sim \chi _{k}^{2}$ asymptotically under $H_{0}$ , where $k$ is the number of constraints imposed by the null hypothesis and

$U({\hat {\theta }}_{0})={\frac {\partial \log L({\hat {\theta }}_{0}|x)}{\partial \theta }}$ and

$I({\hat {\theta }}_{0})=-E\left({\frac {\partial ^{2}\log L({\hat {\theta }}_{0}|x)}{\partial \theta \partial \theta '}}\right).$ ## Special cases

In many situations, the score statistic reduces to another commonly used statistic.

When the data follows a normal distribution, the score statistic is the same as the t statistic.Template:Clarify

When the data consists of binary observations, the score statistic is the same as the chi-squared statistic in the Pearson's chi-squared test.

When the data consists of failure time data in two groups, the score statistic for the Cox partial likelihood is the same as the log-rank statistic in the log-rank test. Hence the log-rank test for difference in survival between two groups is most powerful when the proportional hazards assumption holds.

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