Maximum spacing estimation for multivariate observations under a general class of information-type measures

This article considers the maximum spacing (MSP) method for multivariate observations, nearest neighbour balls are used as a multidimensional analogue to univariate spacings. Compared to the previous studies, a broader class of MSP estimators corresponding to different information-type measures is studied. The studied class of estimators includes also the estimator corresponding to the Kullback-Leibler information measure obtained with the logarithmic function. Consistency of the MSP estimators is proved when the assigned model class is correct, that is the true density belongs to the assigned class. The behaviour of the MSP estimator under different divergence measures is studied and the advantage of using MSP estimators corresponding to different information measures in the context of model validation is illustrated in simulation examples.