kaal:claim:2389423-019

Least squares linear regression is non-robust to outliers: in the presence of outliers its predictions can be dragged toward the outliers and the variance of the estimates can be artificially inflated.

In the presence of outliers, LSLR predictions can be dragged towards the outliers and the variance of the LSLR estimates can be artificially inflated.

Wulf A. Kaal, The Impact of Dodd-Frank Act Compliance Cost on the Hedge Fund Industry (2014), Methodology, p. 10
https://ssrn.com/abstract=2389423 · source PDF

Wulf A. Kaal, The Impact of Dodd-Frank Act Compliance Cost on the Hedge Fund Industry (2014). SSRN: https://ssrn.com/abstract=2389423

• least squares linear regression applied to samples containing outliers

failuresupport: arguedfailure: outlier sensitivity of least squares regressionfamily: research-design-limitationresearch-methods

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