Optimal testing for additivity in multiple nonparametric regression

  • Felix Abramovich*
  • , Italia De Feis
  • , Theofanis Sapatinas
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We consider the problem of testing for additivity in the standard multiple nonparametric regression model. We derive optimal (in the minimax sense) non- adaptive and adaptive hypothesis testing procedures for additivity against the composite nonparametric alternative that the response function involves interactions of second or higher orders separated away from zero in L 2([0, 1] d )-norm and also possesses some smoothness properties. In order to shed some light on the theoretical results obtained, we carry out a wide simulation study to examine the finite sample performance of the proposed hypothesis testing procedures and compare them with a series of other tests for additivity available in the literature.

Original languageEnglish
Pages (from-to)691-714
Number of pages24
JournalAnnals of the Institute of Statistical Mathematics
Volume61
Issue number3
DOIs
StatePublished - Sep 2009

Funding

Funders
School of Mathematical Sciences of Tel Aviv University

    Keywords

    • Additive models
    • Functional hypothesis testing
    • Minimax testing
    • Nonparametric regression
    • Wavelets

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