Asymptotic normality of generalized maximum spacing estimators for multivariate observations

Abstract

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.

Keywords

asymptotic normality; consistency; divergence measures; maximum spacing estimation; nearest neighbor balls

Published in

Scandinavian Journal of Statistics
2020, volume: 47, number: 3, pages: 968-989

SLU Authors

• Kuljus, Kristi

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

• Ranneby, Bo

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1111/sjos.12436

Permanent link to this page (URI)

https://res.slu.se/id/publ/103114