Details

Autor(en) / Beteiligte

• Fan, Yingying
• James, Gareth M.
• Radchenko, Peter

Titel

FUNCTIONAL ADDITIVE REGRESSION

Ist Teil von

• The Annals of statistics, 2015-10, Vol.43 (5), p.2296-2325

Ort / Verlag

Hayward: Institute of Mathematical Statistics

Erscheinungsjahr

2015

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• We suggest a new method, called Functional Additive Regression, or FAR, for efficiently performing high-dimensional functional regression. FAR extends the usual linear regression model involving a functional predictor, X(t), and a scalar response, Y, in two key respects. First, FAR uses a penalized least squares optimization approach to efficiently deal with highdimensional problems involving a large number of functional predictors. Second, FAR extends beyond the standard linear regression setting to fit general nonlinear additive models. We demonstrate that FAR can be implemented with a wide range of penalty functions using a highly efficient coordinate descent algorithm. Theoretical results are developed which provide motivation for the FAR optimization criterion. Finally, we show through simulations and two real data sets that FAR can significantly outperform competing methods.