In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares of residuals in ordinary least squares to cases where model-fitting is achieved by maximum likelihood. It plays an important role in exponential dispersion models and generalized linear models.
The total deviance of a model with predictions of the observation is the sum of its unit deviances: .
Here denotes the fitted values of the parameters in the model M0, while denotes the fitted parameters for the saturated model: both sets of fitted values are implicitly functions of the observations y. Here, the saturated model is a model with a parameter for every observation so that the data are fitted exactly. This expression is simply 2 times the log-likelihood ratio of the full model compared to the reduced model. The deviance is used to compare two models – in particular in the case of generalized linear models (GLM) where it has a similar role to residual variance from ANOVA in linear models (RSS).
Suppose in the framework of the GLM, we have two nested models, M1 and M2. In particular, suppose that M1 contains the parameters in M2, and k additional parameters. Then, under the null hypothesis that M2 is the true model, the difference between the deviances for the two models follows an approximate chi-squared distribution with k-degrees of freedom.
Some usage of the term "deviance" can be confusing. According to Collett:
- "the quantity is sometimes referred to as a deviance. This is [...] inappropriate, since unlike the deviance used in the context of generalized linear modelling, does not measure deviation from a model that is a perfect fit to the data." However, since the principal use is in the form of the difference of the deviances of two models, this confusion in definition is unimportant.
Hypothesis testing on the deviance can use Wilks' theorem.
The unit deviance for the Poisson distribution is , the unit deviance for the Normal distribution is given by .
- Jørgensen, B. (1997). The Theory of Dispersion Models. Chapman & Hall.
- Song, Peter X. -K. (2007). Correlated Data Analysis: Modeling, Analytics, and Applications. Springer Series in Statistics. doi:10.1007/978-0-387-71393-9.
- Nelder, J.A.; Wedderburn, R.W.M. (1972). "Generalized Linear Models". Journal of the Royal Statistical Society. Series A (General). 135 (3): 370–384. doi:10.2307/2344614. JSTOR 2344614.
- McCullagh and Nelder (1989): page 17
- Collett (2003): page 76
- McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Chapman & Hall/CRC. ISBN 0-412-31760-5.