Scoring algorithm, also known as Fisher's scoring, is a form of Newton's method used in statistics to solve maximum likelihood equations numerically, named after Ronald Fisher.

Sketch of derivation

be random variables, independent and identically distributed with twice differentiable p.d.f., and we wish to calculate the maximum likelihood estimator, and consider a Taylor expansion of the score function:

where

is the observed information matrix and rearranging gives us:

We therefore use the algorithm

.

Fisher scoring

, the Fisher information, thus giving us the Fisher Scoring Algorithm:

1. ..

is a consistent estimator (the correction after a single step) is 'optimal' in the sense that its error distribution is asymptotically identical to that of the true max-likelihood estimate.

See also

• Score (statistics)

• Score test

• Fisher information

References

Further reading