TITLE:
Pseudodistance Methods Using Simultaneously Sample Observations and Nearest Neighbour Distance Observations for Continuous Multivariate Models
AUTHORS:
Andrew Luong
KEYWORDS:
Goodness-of-Fit Statistics, Robust Estimators, Multivariate Density Estimate, Information Matrix, Model Testing
JOURNAL NAME:
Open Journal of Statistics,
Vol.9 No.4,
August
13,
2019
ABSTRACT: Using the fact that a multivariate random sample of n observations also
generates n nearest neighbour distance (NND) univariate observations and from
these NND observations, a set of n auxiliary observations can be obtained and
with these auxiliary observations when combined with the original multivariate
observations of the random sample, a
class of pseudodistanceDhis allowed to be used and inference methods can be developed using this class of
pseudodistances. The Dhestimators obtained
from this class can achieve high efficiencies and have robustness properties.
Model testing also can be handled in a unified way by means of goodness-of-fit
tests statistics derived from this class which have an asymptotic normal
distribution. These properties make the developed inference methods relatively
simple to implement and appear to be suitable for analyzing multivariate data
which are often encountered in applications.