In this chapter we consider methods for choosing the "best" model from a class of multiple regression models using what are called variable selection methods. Interestingly, while there is little agreement on how to define "best," there is general agreement in the statistics literature on the consequences of variable selection on subsequent inferential procedures, (i.e., tests and confidence intervals).
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Sheather, . (2009). Variable Selection . In: A Modern Approach to Regression with R. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09608-7_7
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DOI: https://doi.org/10.1007/978-0-387-09608-7_7
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