Estimating smooth monotone functions

被引:197
|
作者
Ramsay, JO [1 ]
机构
[1] McGill Univ, Dept Psychol, Montreal, PQ H3A 1B1, Canada
关键词
convex functions; density estimation; generalized additive model; linear differential equation; monotonicity; nonparametric regression; regression spline; spline smoothing;
D O I
10.1111/1467-9868.00130
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
Many situations call for a smooth strictly monotone function f of arbitrary flexibility. The family of functions defined by the differential equation D(2)f = w Df, where w is an unconstrained coefficient function, comprises the strictly monotone twice differentiable functions. The solution to this equation is f = C-0 + C-1 D-1{exp(D(-1)w)}, where C-0 and C-1 are arbitrary constants and D-1 is the partial integration operator. A basis for expanding w is suggested that permits explicit integration in the expression of f. In fitting data, it is also useful to regularize f by penalizing the integral of w(2) since this is a measure of the relative curvature in f. Applications are discussed to monotone nonparametric regression, to the transformation of the dependent variable in non-linear regression and to density estimation.
引用
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页码:365 / 375
页数:11
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