Estimating prediction error : cross-validation vs. accumulated prediction error

• We study the validation of prediction rules such as regression models and classification algorithms through two out-of-sample strategies, cross-validation and accumulated prediction error. We use the framework of Efron (1983) where measures of prediction errors are defined as sample averages of expected errors and show through exact finite sample calculations that cross-validation and accumulated prediction error yield different smoothing parameter choices in nonparametric regression. The difference in choice does not vanish as sample size increases.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

Local polynomial regression

Nonparametric regression

Out-of-sample validation

Smoothing parameter

Statistics

Statistik

Statistics

statistik

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)