BibTex Citation Data :
@article{Medstat37096, author = {Hesikumalasari Hesikumalasari and I Budiantara and Vita Ratnasari and Khaerun Nisa'}, title = {ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL}, journal = {MEDIA STATISTIKA}, volume = {15}, number = {1}, year = {2022}, keywords = {Semiparametric regression;mixed estimator;linear truncated spline;kernel;multivariable}, 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. }, issn = {2477-0647}, pages = {12--23} doi = {10.14710/medstat.15.1.12-23}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/37096} }
Refworks Citation Data :
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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