阿卡克信息准则
平滑的
数学
花键(机械)
平滑样条曲线
相关性
对比度(视觉)
选型
统计
均方误差
信息标准
回归
应用数学
计算机科学
人工智能
几何学
结构工程
样条插值
工程类
双线性插值
作者
Tatyana Krivobokova,Göran Kauermann
标识
DOI:10.1198/016214507000000978
摘要
AbstractWe investigate the behavior of data-driven smoothing parameters for penalized spline regression in the presence of correlated data. It has been shown for other smoothing methods that mean squared error minimizers, such as (generalized) cross-validation or the Akaike information criterion, are extremely sensitive to misspecifications of the correlation structure resulting in over- or (under-)fitting the data. In contrast to this, we show that a maximum likelihood–based choice of the smoothing parameter is more robust and that for a moderately misspecified correlation structure over- or (under-)fitting does not occur. This is demonstrated in simulations and data examples and is supported by theoretical investigations.KEY WORDS: Correlation structure misspecificationLinear mixed modelSmoothing parameter selection
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