New efficient spline estimation for varying-coefficient models with two-step knot number selection

被引:0
|
作者
Jin, Jun [1 ]
Ma, Tiefeng [1 ]
Dai, Jiajia [2 ]
机构
[1] Southwestern Univ Finance & Econ, Ctr Stat Res, Sch Stat, Chengdu 661130, Peoples R China
[2] Guizhou Univ, Sch Math & Stat, Guiyang 550025, Guizhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Varying-coefficient models; Asymptotic normality; B-spline; Adaptive knot selection; Two-step B-spline; VARIABLE SELECTION; EMPIRICAL LIKELIHOOD; LOCAL ASYMPTOTICS;
D O I
10.1007/s00184-020-00798-8
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
One of the advantages for the varying-coefficient model is to allow the coefficients to vary as smooth functions of other variables and the coefficients functions can be estimated easily through a simple B-spline approximations method. This leads to a simple one-step estimation procedure. We show that such a one-step method cannot be optimal when some coefficient functions possess different degrees of smoothness. Under the regularity conditions, the consistency and asymptotic normality of the two step B-spline estimators are also derived. A few simulation studies show that the gain by the two-step procedure can be quite substantial. The methodology is illustrated by an AIDS data set.
引用
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页码:693 / 712
页数:20
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