DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection)

Classical techniques for choosing important covariates to
include in a model of complex multivariate data relied on various types
of stepwise variable selection processes, see Chapter 16.
These tend to improve prediction accuracy in certain situations, e.g.,
when a small number of features are strongly predictive, or associated,
with the clinical outcome or biosocial trait. However, the prediction
error may be large when the model relies purely on a fidelity term.
Including a regularization term in the optimization of the cost function
improves the prediction accuracy. For example, below we show that by
shrinking large regression coefficients, ridge regularization reduces
overfitting and improves prediction error. Similarly, the least absolute
shrinkage and selection operator (LASSO) employs regularization to
perform simultaneous parameter estimation and variable selection. LASSO
enhances the prediction accuracy and provides a natural interpretation
of the resulting model. Regularization refers to forcing
certain characteristics of model-based scientific inference, e.g.,
discouraging complex models or extreme explanations, even if they fit
the data, enforcing model generalizability to prospective data, or
restricting model overfitting of accidental samples.

In this chapter, we extend the mathematical foundation we presented in Chapter 4
and (1) discuss computational protocols for handling complex
high-dimensional data, (2) illustrate model estimation by controlling
the false-positive rate of selection of salient features, and (3) derive
effective forecasting models.

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