Statistical test based on the gradient of the likelihood function
Not to be confused with Test score.
In statistics, the score test assesses constraints on statistical parameters based on the gradient of the likelihood function—known as the score—evaluated at the hypothesized parameter value under the null hypothesis. Intuitively, if the restricted estimator is near the maximum of the likelihood function, the score should not differ from zero by more than sampling error. While the finite sample distributions of score tests are generally unknown, they have an asymptotic χ2-distribution under the null hypothesis as first proved by C. R. Rao in 1948,[1] a fact that can be used to determine statistical significance.
Since function maximization subject to equality constraints is most conveniently done using a Lagrangean expression of the problem, the score test can be equivalently understood as a test of the magnitude of the Lagrange multipliers associated with the constraints where, again, if the constraints are non-binding at the maximum likelihood, the vector of Lagrange multipliers should not differ from zero by more than sampling error. The equivalence of these two approaches was first shown by S. D. Silvey in 1959,[2] which led to the name Lagrange multiplier test that has become more commonly used, particularly in econometrics, since Breusch and Pagan's much-cited 1980 paper.[3]
The main advantage of the score test over the Wald test and likelihood-ratio test is that the score test only requires the computation of the restricted estimator.[4] This makes testing feasible when the unconstrained maximum likelihood estimate is a boundary point in the parameter space.[citation needed] Further, because the score test only requires the estimation of the likelihood function under the null hypothesis, it is less specific than the likelihood ratio test about the alternative hypothesis.[5]
^Rao, C. Radhakrishna (1948). "Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation". Mathematical Proceedings of the Cambridge Philosophical Society. 44 (1): 50–57. Bibcode:1948PCPS...44...50R. doi:10.1017/S0305004100023987.
^Silvey, S. D. (1959). "The Lagrangian Multiplier Test". Annals of Mathematical Statistics. 30 (2): 389–407. doi:10.1214/aoms/1177706259. JSTOR 2237089.
^Breusch, T. S.; Pagan, A. R. (1980). "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics". Review of Economic Studies. 47 (1): 239–253. doi:10.2307/2297111. JSTOR 2297111.
^Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan; Marx, Brian (2013). Regression : Models, Methods and Applications. Berlin: Springer. pp. 663–664. ISBN 978-3-642-34332-2.
^Kennedy, Peter (1998). A Guide to Econometrics (Fourth ed.). Cambridge: MIT Press. p. 68. ISBN 0-262-11235-3.
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