Gaussian process functional regression Modeling for batch data
Gaussian process functional regression Modeling for batch data
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DOI：
10.1111/j.1541-0420.2007.00758.x
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发表时间：
2007-09-01
期刊：
影响因子：
1.9
通讯作者：
Titterington, D. M.
中科院分区：
文献类型：
--
作者：
Shi, J. Q.;Wang, B.;Titterington, D. M.
提出了一种用于批量数据分析的高斯过程函数回归模型。同时考虑协方差结构和均值结构，协方差结构用高斯过程回归模型建模，均值结构用函数回归模型建模。该模型允许在协方差结构和均值结构中包含协变量。它对函数输出变量与一组函数和非函数协变量之间的非线性关系进行建模。几个应用和模拟研究的报告，并表明该方法提供了很好的效果曲线拟合和预测。
A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction.