Simple and efficient improvements of multivariate local linear regression

Ming-Yen Cheng, Liang Peng

Research output: Contribution to journalJournal articlepeer-review

9 Citations (Scopus)


This paper studies improvements of multivariate local linear regression. Two intuitively appealing variance reduction techniques are proposed. They both yield estimators that retain the same asymptotic conditional bias as the multivariate local linear estimator and have smaller asymptotic conditional variances. The estimators are further examined in aspects of bandwidth selection, asymptotic relative efficiency and implementation. Their asymptotic relative efficiencies with respect to the multivariate local linear estimator are very attractive and increase exponentially as the number of covariates increases. Data-driven bandwidth selection procedures for the new estimators are straightforward given those for local linear regression. Since the proposed estimators each has a simple form, implementation is easy and requires much less or about the same amount of effort. In addition, boundary corrections are automatic as in the usual multivariate local linear regression.
Original languageEnglish
Pages (from-to)1501-1524
Number of pages24
JournalJournal of Multivariate Analysis
Issue number7
Publication statusPublished - Aug 2006

User-Defined Keywords

  • Bandwidth selection
  • Kernel smoothing
  • Local linear regression
  • Multiple regression
  • Nonparametric regression
  • Variance reduction


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