ESTIMATION IN A PARTIAL REGRESSION MODEL FOR CONTAMINATED DATA USING THE ROBUST PARTIAL RESIDUAL ESTIMATION TECHNIQUE

被引:0
|
作者
Al-Azzawi, Ekhlass A. [1 ]
Al-Alway, Lekaa A. [2 ]
机构
[1] Univ Karbala, Coll Farmacy, Karbala, Iraq
[2] Univ Baghdad, Adhamiya, Baghdad, Iraq
来源
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES | 2022年 / 18卷
关键词
Partial regression model; Outliers; Robustness; Partial residual; Pseudo data; PARTIALLY LINEAR-MODELS; SPLINE; RIDGE; PARAMETERS;
D O I
暂无
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
The partial regression model is a type of semi-parametric regression model that takes the form of a simplified semiparametric regression model. The partial regression model is more flexible than the parametric linear models because it combines parametric and non-parametric components in which the response variable has a linear relationship with some explanatory variables and a non-linear relationship with others. In this study, we examine two estimation methods, the robust partial residuals estimation method, and the non-robust estimation by Nadaraya-Watson smoothing and Speakman estimation, and compare them using mean squared error (MSE) as a comparison criterion for simulation experiments at various sample sizes, contaminated rates and a set of default values for the.. By comparing the two estimators, it became evident that the robust partial residuals are more efficient than the non-robust estimator since, it has the lowest mean square error (MSE) and is real parameter values approximate the default parameter values.
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页码:1473 / 1482
页数:10
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