Scoring algorithm

Biologist and statistician Ronald Fisher

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

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

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

• Score (statistics)

References

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