SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION.
SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION.
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DOI：
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发表时间：
2009-05
影响因子：
1.4
通讯作者：
Hyune-Ju Kim;Binbing Yu;E. Feuer
中科院分区：
文献类型：
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作者：
Hyune-Ju Kim;Binbing Yu;E. Feuer
分段线回归在许多应用中得到了应用，Kim等人（2000）讨论了分段线回归中变化点数量的估计问题。本文研究了Kim et al.（2000）的置换过程所选择的变点数目的渐近性质。该程序基于似然比类型检验的顺序应用，并通过其设计控制过拟合概率。本文证明了在一定条件下，置换过程所选择的变点数目是一致的。通过仿真，将置换过程与基于信息的贝叶斯信息准则（BIC）、赤池信息准则（AIC）和广义交叉验证（GCV）进行了比较。
Segmented line regression has been used in many applications, and the problem of estimating the number of change-points in segmented line regression has been discussed in Kim et al. (2000). This paper studies asymptotic properties of the number of change-points selected by the permutation procedure of Kim et al. (2000). This procedure is based on a sequential application of likelihood ratio type tests, and controls the over-fitting probability by its design. In this paper we show that, under some conditions, the number of change-points selected by the permutation procedure is consistent. Via simulations, the permutation procedure is compared with such information-based criterior as the Bayesian Information Criterion (BIC), the Akaike Information Criterion (AIC), and Generalized Cross Validation (GCV).