Multinomial test

In statistics, the multinomial test is the likelihood-ratio test of the null hypothesis that the parameters of a multinomial distribution equal specified values. It is used for categorical data; see Read and Cressie.

We begin with a sample of $N$ items each of which has been observed to fall into one of $k$ categories. We can define $\mathbf {x} =(x_{1},x_{2},\dots ,x_{k})$ as the observed numbers of items in each cell. Hence $\textstyle \sum _{i=1}^{k}x_{i}=N$.

Next, we define a vector of parameters $H_{0}:\mathbf {\pi } =(\pi _{1},\pi _{2},\dots ,\pi _{k})$, where $\textstyle \sum _{i=1}^{k}\pi _{i}=1$. These are the parameter values under the null hypothesis.

The exact probability of the observed configuration $\mathbf {x}$ under the null hypothesis is given by

$\Pr(\mathbf {x_{0}} )=n!\prod _{i=1}^{k}{\frac {\pi _{i}^{x_{i}}}{x_{i}!}}.$

Under the alternative hypothesis, each value $\pi _{i}$ is replaced by its unconstrained maximum likelihood estimate $p_{i}=x_{i}/N$ and the exact probability of the observed configuration $\mathbf {x}$ under the alternative hypothesis is given by

$\Pr(\mathbf {x_{A}} )=n!\prod _{i=1}^{k}{\frac {p_{i}^{x_{i}}}{x_{i}!}}.$

The natural logarithm of the ratio between these two probabilities multiplied by -2 is then the log likelihood ratio statistic

$-2\ln(LR)=\textstyle -2\sum _{i=1}^{k}x_{i}\ln(\pi _{i}/p_{i}).$

If the null hypothesis is true, then the observed number of cases falling into category i will differ from $N\pi _{i}$ only by error variation. In this case, as N increases, the distribution of $-2\ln(LR)$ converges to that of chisquare with k-1 degrees of freedom when the null hypothesis is true. However it has long been known (eg Lawley 1956) that for finite sample sizes, the moments of $-2\ln(LR)$ are greater than those of chisquare, thus inflating the probability of Type I errors (false positives). To address this, Williams (1976) showed that the test statistic converges faster to its asymptote if it is multiplied by a factor given by

$q=1+{\frac {\sum _{i=1}^{k}1/\pi _{i}-1}{6N(k-1)}}.$

In the special case where the null hypothesis is that all the values $\pi _{i}$ are equal to 1/k (ie it stipulates a uniform distribution), this simplifies to

$q=1+{\frac {k+1}{6N}}.$

Subsequently, Smith et al (1981) suggested that

References

• Lawley, D. N. (1956). "A General Method of Approximating to the Distribution of Likelihood Ratio Criteria". Biometrika. 43: 295–303.
• Smith, P. J., Rae, D. S., Manderscheid, R. W. and Silbergeld, S. (1981). "Approximating the Moments and Distribution of the Likelihood Ratio Statistic for Multinomial Goodness of Fit". Journal of the American Statistical Association. 76: 737–740.{{cite journal}}: CS1 maint: multiple names: authors list (link)
• Williams, D. A. (1976). "Improved Likelihood Ratio Tests for Complete Contingency Tables". Biometrika. 63: 33–37.