Change-point estimation using shape-restricted regression splines

被引:6
|
作者
Liao, Xiyue [1 ]
Meyer, Mary C. [1 ]
机构
[1] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
关键词
Mode estimation; Inflection point; Monotone; Convex; Convergence rate; CONVERGENCE; PENALTIES; RATES;
D O I
10.1016/j.jspi.2017.03.007
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
We consider estimating a regression function f(m) and a change-point m, where m is a mode or an inflection point. For a given m, the least-squares estimate of fm is found using constrained regression splines, then the set of possible change-points is searched to find the overall least-squares (m) over cap Convergence rates are obtained for each type of change-point estimator, and simulations show that these methods compare well to existing methods. Extensions to the partial linear model and to the case of correlated errors are straightforward, and a penalized spline version is also provided. The methods are available in the R package ShapeChange. (C) 2017 Elsevier B.V. All rights reserved.
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页码:8 / 21
页数:14
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