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.

中文翻译：

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多元观测的广义最大间距估计量的渐近正态性

在本文中，对于多元观测，考虑了最大间隔法。最近邻球用作单变量间距的多维模拟。一类信息类型的度量用于概括最大间距估计量的概念。当分配的模型类正确时，证明这些广义最大间距估计量的渐近正态性，即真实密度是模型类的成员。