Prediction of Euclidean distances with discrete and continuous outcomes

Authors:
F. Mortier;S. Robin;S. Lassalvy;C. P. Baril;A. Bar-Hen
Affiliations:
Cirad-forêt/plantations, Montpellier Cedex, France;Institut National Agronomique Paris-Grignon, Paris Cedex, France;-;GEVES La Minière, Guyancourt cedex, France;Faculté de Saint Jérôme IMEP, Université Aix-Marseille III, Marseille cedex, France
Venue:
Journal of Multivariate Analysis
Year:
2006

Citing 2
Cited 1

Generalization of the Mahalanobis distance in the mixed case

Journal of Multivariate Analysis
A generalized Mahalanobis distance for mixed data

Journal of Multivariate Analysis

Multivariate dynamic model for ordinal outcomes

Journal of Multivariate Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

The objective of this paper is first to predict generalized Euclidean distances in the context of discrete and quantitative variables and then to derive their statistical properties. We first consider the simultaneous modelling of discrete and continuous random variables with covariates and obtain the likelihood. We derive an important property useful for its practical maximization. We then study the prediction of any Euclidean distances and its statistical proprieties, especially for the Mahalanobis distance. The quality of distance estimation is analyzed through simulations. This results are applied to our motivating example: the official distinction procedure of rapeseed varieties.