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Model of Robust Regression with Parametric and Nonparametric Methods

机译：参数和非参数方法的鲁棒回归模型

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In the present work, we evaluate the performance of the classical parametric estimation method "ordinary least squares" with the classical nonparametric estimation methods, some robust estimation methods and two suggested methods for conditions in which varying degrees and directions of outliers are presented in the observed data. The study addresses the problem via computer simulation methods. In order to cover the effects of various situations of outliers on the simple linear regression model, samples were classified into four cases (no outliers, outliers in the X-direction, outliers in the Y-direction and outliers in the XY-direction) and the percentages of outliers are varied between 10%, 20% and 30%. The performances of estimators are evaluated in respect to their mean squares error and relative mean squares error. Keywords: Simple Linear Regression model; Ordinary Least Squares Method; Nonparametric Regression; Robust Regression; Least Absolute Deviations Regression; M-Estimation Regression; Trimmed Least Squares Regression.
机译：在目前的工作中，我们用经典的非参数估计方法，一些鲁棒的估计方法和两种建议的方法来评估经典参数估计方法“普通最小二乘”的性能，其中所观察到的异常点的程度和方向有所不同数据。该研究通过计算机仿真方法解决了这个问题。为了涵盖离群值的各种情况对简单线性回归模型的影响，将样本分为四种情况（无离群值，X方向的离群值，Y方向的离群值和XY方向的离群值）和离群值的百分比在10％，20％和30％之间变化。评估器的性能根据其均方误差和相对均方误差进行评估。关键词：简单线性回归模型普通最小二乘法非参数回归稳健的回归;最小绝对偏差回归;移动估计回归;修剪最小二乘回归。

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