Flexible Copula Density Estimation with Penalized Hierarchical B-splines

被引：43

作者：

Kauermann, Goeran ^{[1
]}

Schellhase, Christian ^{[2
]}

Ruppert, David ^{[3
]}

机构：

[1] Univ Munich, Dept Stat, D-80539 Munich, Germany

[2] Univ Bielefeld, Dept Business Adm & Econ, Bielefeld, Germany

[3] Cornell Univ, Sch Operat Res & Informat Engn, Ithaca, NY 14853 USA

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2013年 / 40卷 / 04期

基金：

美国国家科学基金会;

关键词：

copula density estimation; hierarchical B-splines; penalized spline smoothing; quadratic programming; SEMIPARAMETRIC REGRESSION; NONPARAMETRIC-ESTIMATION; WEAK-CONVERGENCE; MODEL;

D O I：

10.1111/sjos.12018

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The paper introduces a new method for flexible spline fitting for copula density estimation. Spline coefficients are penalized to achieve a smooth fit. To weaken the curse of dimensionality, instead of a full tensor spline basis, a reduced tensor product based on so called sparse grids (Notes Numer. Fluid Mech. Multidiscip. Des., 31, 1991, 241-251) is used. To achieve uniform margins of the copula density, linear constraints are placed on the spline coefficients, and quadratic programming is used to fit the model. Simulations and practical examples accompany the presentation.

引用

页码：685 / 705

页数：21

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