January 26, 2013

Lecture: Smoothing Methods in Regression (Advanced Data Analysis from an Elementary Point of View)

Lecture 4: The bias-variance trade-off tells us how much we should smooth. Some heuristic calculations with Taylor expansions for general linear smoothers. Adapting to unknown roughness with cross-validation; detailed examples. How quickly does kernel smoothing converge on the truth? Using kernel regression with multiple inputs. Using smoothing to automatically discover interactions. Plots to help interpret multivariate smoothing results. Average predictive comparisons.

Reading: Notes, chapter 4 (R)
Optional readings: Faraway, section 11.1; Hayfield and Racine, "Nonparametric Econometrics: The np Package"; Gelman and Pardoe, "Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components" [PDF]

Advanced Data Analysis from an Elementary Point of View

Posted at January 26, 2013 21:37 | permanent link

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