Abstract
Regression models are extremely important in describing relationships between variables. Linear regression is a simple, but powerful tool in investigating linear dependencies. It relies, however, on strict distributional assumptions. Nonparametric regression models are widely used, because fewer assumptions about the data at hand are necessary.
Everything must be made as simple as possible. But not simpler.
— Albert Einstein
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Härdle, W.K., Okhrin, O., Okhrin, Y. (2017). Regression Models. In: Basic Elements of Computational Statistics. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-55336-8_7
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DOI: https://doi.org/10.1007/978-3-319-55336-8_7
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-55336-8
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