Index > Glossary

Score vector

by Marco Taboga, PhD

In the theory of maximum likelihood estimation, the score vector (or simply, the score) is the gradient (i.e., the vector of first derivatives) of the log-likelihood function with respect to the parameters being estimated.

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The concept is defined as follows.

Definition Let $	heta $ be a Kx1 parameter vector describing the distribution of a sample $xi $. Let [eq1] be the likelihood function of the sample $xi $, depending on the parameter $	heta $. Let [eq2] be the log-likelihood function[eq3]Then, the Kx1 vector of first derivatives of [eq4] with respect to the entries of $	heta $, denoted by [eq5]is called the score vector.

The symbol $
abla $ is read nabla and is often used to denote the gradient of a function.


In the next example, the likelihood depends on a $2	imes 1$ parameter. As a consequence, the score is a $2	imes 1$ vector.

Example Suppose the sample $xi $ is a vector of n draws $x_{1}$, ..., $x_{n}$ from a normal distribution with mean mu and variance sigma^2. As proved in the lecture on maximum likelihood estimation of the parameters of a normal distribution, the log-likelihood of the sample is [eq6]The two parameters (mean and variance) together form a $2	imes 1$ vector[eq7]The partial derivative of the log-likelihood with respect to mu is [eq8]and the partial derivative with respect to the variance sigma^2 is [eq9]The score vector is[eq10]

How the score is used to find the maximum likelihood estimator

The maximum likelihood estimator $widehat{	heta }$ of the parameter $	heta $ solves the maximization problem[eq11]

Under some regularity conditions, the solution of this problem can be found by solving the first order condition[eq12]that is, by equating the score vector to 0.

More details

More details about the log-likelihood and the score vector can be found in the lecture entitled Maximum likelihood.

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