TITLE:
Minimum Quadratic Distance Methods Using Grouped Data for Parametric Families of Copulas
AUTHORS:
Andrew Luong
KEYWORDS:
Influence Functions, Quasi-Monte Carlo Numbers, Chi-Square Tests Statistics, Random Cells, Contingency Tables
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.3,
June
7,
2018
ABSTRACT: Minimum quadratic distance (MQD) methods are used to
construct chi-square test statistics for simple and composite hypothesis for
parametric families of copulas. The methods aim at grouped data which form a contingency
table but by defining a rule to group the data using Quasi-Monte Carlo numbers
and two marginal empirical quantiles, the methods can be extended to handle complete data. The rule implicitly defines points on the
nonnegative quadrant to form quadratic distances and the similarities of the
rule with the use of random cells for classical minimum chi-square methods are
indicated. The methods are relatively simple to implement and
might be useful for applied works in various fields such as actuarial science.