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

• 作者: Andrew Luong
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Influence Functions Quasi-Monte Carlo Numbers CHI-SQUARE Tests Statistics Random Cells CONTINGENCY Tables
• 年，卷（期）: 2018,（3）
• 页码范围: 427-456
• 页数: 30页
• 分类号: R73

摘要

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.