  In this paper, the maximum spacing method is considered for multivariate
observations. Nearest neighbour balls are used as a multidimensional analogue
to univariate spacings. A class of information-type measures is used to
generalize the concept of maximum spacing estimators. Asymptotic normality of
these generalized maximum spacing estimators is proved when the assigned model
class is correct, that is the true density is a member of the model class.
