𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Variable selection by stepwise slicing in nonparametric regression

✍ Scribed by K.B. Kulasekera

Book ID
104301542
Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
146 KB
Volume
51
Category
Article
ISSN
0167-7152

No coin nor oath required. For personal study only.

✦ Synopsis

We consider variable selection issue in a nonparametric regression setting. Two stepwise procedures based on variance estimators are proposed for selecting the signiΓΏcant variables in a general nonparametric regression model. These procedures do not require multidimensional smoothing at intermediate steps and they are based on formal tests of hypotheses as opposed to existing methods in the literature. Asymptotic properties are examined and empirical results are given.

πŸ“œ SIMILAR VOLUMES

A note on variable selection in nonparam
A note on variable selection in nonparametric regression with dependent data
✍ Wenceslao GonzΓ‘lez-Manteiga; Alejandro Quintela-del-Rı́o; Philippe Vieu πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 128 KB

We develop a nonparametric test, based on kernel smoothers, in order to decide whether some covariates could be suppressed in a multidimensional nonparametric regression study. We give the asymptotic distribution of the statistic involved in our test, under a general dependence assumption on the sam