Variable selection in linear regression in the presence of outliers

Kamble Tejaswi S.; Kashid Dattatraya N.

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Variable selection in linear regression in the presence of outliers

机译：存在离群值时线性回归中的变量选择

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摘要

Majority variable selection methods are based on ordinary least squares (OLS) parameter estimation method. The performance of these variable selection methods is not satisfactory in the presence of outlier observations in the data. Only few variable selection methods based on other parameter estimation methods like M-estimator are proposed by the researchers. In this paper, we propose variable selection method using sum of transformed residual based on the M-estimator in the presence of outlier observation(s). The performance of the proposed method is evaluated through real data and simulated data.

机译：多数变量选择方法基于普通最小二乘（OLS）参数估计方法。在数据中存在异常观察值的情况下，这些变量选择方法的性能并不令人满意。研究人员仅提出了基于其他参数估计方法（例如M估计器）的变量选择方法。在本文中，我们提出了在存在异常观测值的情况下使用基于M估计量的变换残差之和的变量选择方法。通过实际数据和仿真数据对所提方法的性能进行了评估。

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来源
《International journal of data analysis techniques and strategies》 |2017年第2期|167-188|共22页
作者
Kamble Tejaswi S.; Kashid Dattatraya N.;
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作者单位

Department of Statistics, Shivaji University, Kolhapur (MS), India;

Department of Statistics, Shivaji University, Kolhapur (MS), India;

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正文语种 eng
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关键词
M-estimator; Outlier; Sum of transformed residuals; Variable selection;

机译：M估计器;离群值转换后的残差之和;变量选择;

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专利

1. Simultaneous variable selection and outlier identification in linear regression using the mean-shift outlier model [J] . Sung-Soo Kim, Sung H. Park, W.J. Krzanowski Journal of applied statistics . 2008,第3a4期

机译：使用均值漂移离群模型的线性回归中的同时变量选择和离群值识别
2. Subset selection in multiple linear regression in the presence of outlier and multicollinearity [J] . Nileshkumar H.Jadhav, Dattatraya N. Kashid, Subhash R. Kulkarni Statistical Methodology . 2014,第JULa期

机译：存在离群和多重共线性的多元线性回归中的子集选择
3. Genetic algorithms for outlier detection and variable selection in linear regression models [J] . Tolvi J Soft computing: A fusion of foundations, methodologies and applications . 2004,第8期

机译：线性回归模型中离群值检测和变量选择的遗传算法
4. Outlier Detection and Robust Variable Selection for Least Angle Regression [C] . Shirin Shahriari, Susana Faria, A. Manuela Goncalves, International conference on computational science and its applications . 2014

机译：最小角度回归的离群值检测和鲁棒变量选择
5. Outlier detection and multicollinearity in sequential variable selection: A least angle regression-based approach. [D] . Kirtland, Kelly Meredith. 2017

机译：顺序变量选择中的异常值检测和多重共线性：基于最小角度回归的方法。
6. Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate [O] . Harvey J Motulsky, Ronald E Brown 2006

机译：用非线性回归拟合数据时检测异常值–基于鲁棒非线性回归和错误发现率的新方法
7. Outlier Detection and Multicollinearity in Sequential Variable Selection: A Least Angle Regression-Based Approach [O] . Kirtland Kelly Meredith 2017

机译：序列变量选择中的离群值检测和多重共线性：基于最小角度回归的方法
8. Multiple Outliers in Linear Regression: Advances in Detection Methods, Robust Estimation, and Variable Selection [R] . Wisnowski, J. W. 1999

机译：线性回归中的多个异常值：检测方法，稳健估计和变量选择的进展

Variable selection in linear regression in the presence of outliers

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