A generalized goal programming model for parsimonious robust clusterwise linear regression

被引:2
|
作者
Ismail, Eman [1 ]
Rashwan, Mahmoud [1 ]
Makary, Nadia [1 ]
机构
[1] Cairo Univ, Fac Econ & Polit Sci, Dept Stat, Giza 12613, Egypt
来源
关键词
Goal programming; Variable selection; Outlier detection; Clusterwise regression; Simulation; Data analysis; VARIABLE SELECTION; FAST ALGORITHM;
D O I
10.1080/09720510.2018.1522801
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In the last decades, clusterwise regression has received considerable attention. Existence of outliers in the dataset and/or usage of unimportant explanatory variables in fitting the regression lines can lead to inaccurate clustering and regression lines. In this paper, we propose a Mixed Integer Programming (MIP) model to obtain a parsimonious robust clusterwise linear regression. The proposed model also can determine the number of homogenous clusters in the dataset and detect the outliers. The proposed model is applied on some datasets and the results are promising. Also, the performance of the model is evaluated using a simulation study.
引用
收藏
页码:51 / 71
页数:21
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