Ordinary Least Squares (OLS) models are popular tools among field scientists, because they are easy to understand and use. Although OLS estimators are unbiased, it is often advantageous to introduce some bias in order to lower the overall variance in a model. This study focuses on comparing ridge regression and the LASSO methods which both introduce bias to the regression problem. Both approaches are modeled after the OLS but also implement a tuning parameter. Additionally, this study will compare the use of two different functions in R, one of which will be used for ridge regression and the LASSO while the other will be used strictly for the LASSO. The techniques discussed are applied to a real set of data involving some physiochemical properties of wine and how they affect the overall quality of the wine.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-1472 |
| Date | 10 August 2018 |
| Creators | Phillips, Katie Lynn |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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