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Comparison of partial least squares with other prediction methods via generated data

机译:通过生成数据对其他预测方法的偏最小二乘比较

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The purpose of this study is to compare the Partial Least Squares (PLS), Ridge Regression (RR) and Principal Components Regression (PCR) methods, used to fit regressors with severe multicollinearity against a dependent variable. To realize this, a great number of varying groups of datasets are generated from standard normal distribution allowing for the inclusion of different degrees of collinearities for 10000 replications. The design of the study is based on a simulation work that has been performed for six different degrees of multicollinearity levels and sample sizes. From the generated data, a comparison is made using the value of mean squares error of the regression parameters. The findings show that each prediction method is affected by the sample size, number of regressors or multicollinearity level. However, in contrast to literature (sayn200), whatever the number of regressors is, PCR had significantly better results compared to the other two.
机译:本研究的目的是比较部分最小二乘(PLS),RIDGE回归(RR)和主成分回归(PCR)方法,用于拟合具有对依赖变量的严重多色性度的回归。为了实现这一点,从标准正态分布产生大量不同的数据集,允许包含10000复制的不同程度的共线性。该研究的设计基于已经进行了六种不同程度的多色性水平和样本尺寸的模拟工作。从生成的数据中,使用回归参数的均方误差的值进行比较。调查结果表明,每个预测方法受到样本大小,回归数或多型性等级的影响。然而,与文学(Sayn200)相比,无论回归流器数量如何,PCR与其他两个相比具有明显更好的结果。

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