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

Abstract

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.

Keywords

Consistency; Divergence measures; Maximum spacing estimation; Model validation Nearest neighbour; Nearest neighbour balls

Published in

Journal of Multivariate Analysis
2025, volume: 208, article number: 105433
Publisher: ELSEVIER INC

SLU Authors

• Ranneby, Bo

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1016/j.jmva.2025.105433

Permanent link to this page (URI)

https://res.slu.se/id/publ/141461