Minimum Quadratic Distance Methods Using Grouped Data for Parametric Families of Copulas - Open Journal of Statistics

OJS > Vol.8 No.3, June 2018

Minimum Quadratic Distance Methods Using Grouped Data for Parametric Families of Copulas ()

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DOI: 10.4236/ojs.2018.83028 678 Downloads 1,312 Views

Author(s)

Andrew Luong

Affiliation(s)

école d’actuariat, Université Laval, Sainte-Foy, Canada.

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.

KEYWORDS

Influence Functions, Quasi-Monte Carlo Numbers, Chi-Square Tests Statistics, Random Cells, Contingency Tables

Share and Cite:

Luong, A. (2018) Minimum Quadratic Distance Methods Using Grouped Data for Parametric Families of Copulas. Open Journal of Statistics, 8, 427-456. doi: 10.4236/ojs.2018.83028.

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