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.

Source quote, verbatim

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

From

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

Cite as

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

Holds when

least squares linear regression applied to samples containing outliers

Classification

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

Verify

The quote above is an exact substring of the source PDF, whose sha256 is 6b95323abbaffd00a012589531859f2938ab8b372d0243171059ff82e55a0838. Extraction method: pdf-text-layer.
Attestation record: colloquium/attestations/db24ff7108e5d1bf...json
Verify the binding yourself: curl -s https://wulfkaal.github.io/claims/2389423-019.md | sha256sum