Author(s)

Andrew Luong

école d’actuariat, Université Laval, Ste Foy, Québec, Canada.

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 pseudodistance D_h is allowed to be used and inference methods can be developed using this class of pseudodistances. The D_h estimators 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.

Goodness-of-Fit Statistics, Robust Estimators, Multivariate Density Estimate, Information Matrix, Model Testing

Luong, A. (2019) Pseudodistance Methods Using Simultaneously Sample Observations and Nearest Neighbour Distance Observations for Continuous Multivariate Models. Open Journal of Statistics, 9, 445-457. doi: 10.4236/ojs.2019.94030.