estimating probability metrics from samples

I took a look at this interesting paper by Sriperumbudur et al., On the empirical estimation of integral probability metrics (Electronic Journal of Statistics Vol. 6 (2012) pp.1550–1599). The goal of the paper is to estimate a distance or divergence between two distributions P and Q based on samples from each distribution. This sounds pretty vague at first… what kind of distributions? How many samples? This paper looks at integral probability metrics, which have the form

\gamma(P,Q) = \sup_{f \in \mathcal{F}} \left| \int_{S} f dP - \int_{S} f dQ \right|

where S is a measurable space on which P and Q are defined, and \mathcal{F} is a class of real-valued bounded measurable functions on S. This class doesn’t contain Csiszár \phi-divergences (also known as Ali-Silvey distances), but does contain the total variational distance.

Different choices of the function class give rise to different measures of difference used in so-called two-sample tests, such as the Kolmogorov-Smirnov test. The challenge in practically using these tests is that it’s hard to get bounds on how fast an estimator of \gamma(P,Q) converges if we have to estimate it from samples of P and Q. The main result of the paper is to provide estimators with consistency and convergence guarantees. In particular, they estimators are based on either linear programming or (in the case of kernel tests) in closed form.

The second section of the paper connects tests based on IPMs with the risk associated to classification rules for separating P and Q when the separation rule is restricted to come from the function class \mathcal{F} associated to the rule. This is a nice interpretation of these two-sample tests — they are actually doing similar things for restricted classes of classifiers/estimators.

Getting back to KL divergence and non-IPM measures, since total variation gives a lower bound on the KL divergence, they also provide lower bounds on the total variation distance in terms of other IPM metrics. This is important since the total variation distance can’t be estimated itself in a strongly consistent way. This could be useful for algorithms which need to estimate the total variation distance for continuous data. In general, estimating distances between multivariate continuous distributions can become a bit of a mess when you have to use real data — doing a plug-in estimate using, e.g. a kernel density estimator is not always the best way to go, and instead attacking the distance you want to measure directly could yield better results.

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