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Asymptotic normality of generalized maximum spacing estimators for multivariate observations
Author(s) -
Kuljus Kristi,
Ranneby Bo
Publication year - 2020
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12436
Subject(s) - mathematics , estimator , univariate , multivariate statistics , asymptotic distribution , statistics , normality , class (philosophy) , multivariate normal distribution , multivariate analysis , artificial intelligence , computer science
In this paper, the maximum spacing method is considered for multivariate observations. Nearest neighbor 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 of model parameters. 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.