Rao's score test, also known as the score test or the Lagrange multiplier test (LM test) in econometrics, is a statistical test of a simple null hypothesis that a parameter of interest is equal to some particular value . It is the most powerful test when the true value of is close to . 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 constitutes a potential advantage in comparison to other tests, such as the Wald test and the generalized likelihood ratio test (GLRT). This makes testing feasible when the unconstrained maximum likelihood estimate is a boundary point in the parameter space.
Single parameter test
The statistic to test is
which has an asymptotic distribution of , when is true.
Note on notation
Note that some texts use an alternative notation, in which the statistic is tested against a normal distribution. This approach is equivalent and gives identical results.
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The case of a likelihood with nuisance parameters
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As most powerful test for small deviations
Where is the likelihood function, is the value of the parameter of interest under the null hypothesis, and is a constant set depending on the size of the test desired (i.e. the probability of rejecting if is true; see Type I error).
The score test is the most powerful test for small deviations from . To see this, consider testing versus . By the Neyman–Pearson lemma, the most powerful test has the form
Taking the log of both sides yields
The score test follows making the substitution (by Taylor series expansion)
and identifying the above with .
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.
A more general score test can be derived when there is more than one parameter. Suppose that is the maximum likelihood estimate of under the null hypothesis while and are respectively, the score and the Fisher information matrices under the alternative hypothesis. Then
asymptotically under , where is the number of constraints imposed by the null hypothesis and
This can be used to test .
In many situations, the score statistic reduces to another commonly used statistic.
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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