Scoring algorithm

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In statistics, Fisher's scoring algorithm is a form of Newton's method used to solve maximum likelihood equations numerically.

Sketch of Derivation[edit]

Let Y_1,\ldots,Y_n be random variables, independent and identically distributed with twice differentiable p.d.f. f(y; \theta), and we wish to calculate the maximum likelihood estimator (M.L.E.) \theta^* of \theta. First, suppose we have a starting point for our algorithm \theta_0, and consider a Taylor expansion of the score function, V(\theta), about \theta_0:

V(\theta) \approx V(\theta_0) - \mathcal{J}(\theta_0)(\theta - \theta_0), \,

where

\mathcal{J}(\theta_0) = - \sum_{i=1}^n \left. \nabla \nabla^{\top} \right|_{\theta=\theta_0} \log f(Y_i ; \theta)

is the observed information matrix at \theta_0. Now, setting \theta = \theta^*, using that V(\theta^*) = 0 and rearranging gives us:

\theta^* \approx \theta_{0} + \mathcal{J}^{-1}(\theta_{0})V(\theta_{0}). \,

We therefore use the algorithm

\theta_{m+1} = \theta_{m} + \mathcal{J}^{-1}(\theta_{m})V(\theta_{m}), \,

and under certain regularity conditions, it can be shown that \theta_m \rightarrow \theta^*.

Fisher scoring[edit]

In practice, \mathcal{J}(\theta) is usually replaced by \mathcal{I}(\theta)= \mathrm{E}[\mathcal{J}(\theta)], the Fisher information, thus giving us the Fisher Scoring Algorithm:

\theta_{m+1} = \theta_{m} + \mathcal{I}^{-1}(\theta_{m})V(\theta_{m}).

See also[edit]

References[edit]

Jennrich, R. I., & Sampson, P. F. (1976). Newton-Raphson and related algorithms for maximum likelihood variance component estimation. Technometrics, 18, 11-17.