ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL

Hesikumalasari Hesikumalasari, I. Budiantara, V. Ratnasari, Khaerun Nisa'
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Abstract

The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.
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多变量线性截断样条与多变量核混合估计半参数回归曲线
回归分析的响应变量与其中一个变量预测因子呈线性关系,而与其他预测变量的关系模式未知。因此,可以用半参数回归模型来逼近。与响应变量具有线性关系的预测变量可以通过使用称为参数分量的线性参数曲线来逼近。同时,响应变量与另一个预测变量之间的未知关系可以通过称为非参数分量的非参数曲线来逼近。如果非参数分量中的预测变量不止一个,则可以使用不同的非参数曲线,称为组合或混合估计量来逼近它。本文采用多变量线性截尾样条和多变量核的混合估计来逼近非参数分量。本研究的目的是利用多变量截断样条和多变量核的混合估计来估计半参数回归曲线的模型。使用普通最小二乘法对该混合模型进行估计。
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