半参数估计的自然样条函数法  

Semiparametric Regression with Cubic Spline

作　　者：吴云[1] 孙海燕[1] 马学忠[1] 

机构地区：[1]武汉大学测绘学院,武汉市珞喻路129号430079

出　　处：《武汉大学学报（信息科学版）》2004年第5期398-401,共4页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目 ( 4 0 2 740 0 5 )

摘　　要：用补偿最小二乘原理 ,得到了参数和非参数分量的惟一解 ,并通过模拟计算 ,对半参数回归模型和参数模型的计算结果进行了比较。结果表明 。Systematic errors contained in observations are always complicated smooth function varying with some variables. This paper describes this systematic errors using natural cubic spline, which is nonparametric component in semiparametric regression model. Penalised least squares technique implemented in the procedure reduces to unique solution. According to simulating tests, the semiparametric regression model and the penalised least squares technique can better separate systematic errors from observations compared with the parametric model and the least squares technique.

关 键 词：半参数回归 系统误差 自然样条函数 平滑参数 补偿最小二乘原理 

分 类 号：P207.1[天文地球—测绘科学与技术]