Local polynomial estimation of regression functions for mixing processes

Elias Masry, Jianqing Fan

Research output: Contribution to journalArticle

119 Scopus citations

Abstract

Local polynomial fitting has many exciting statistical properties which where established under i.i.d. setting. However, the need for non-linear time series modeling, constructing predictive intervals, understanding divergence of non-linear time series requires the development of the theory of local polynomial fitting for dependent data. In this paper, we study the problem of estimating conditional mean functions and their derivatives via a local polynomial fit. The functions include conditional moments, conditional distribution as well as conditional density functions. Joint asymptotic normality for derivative estimation is established for both strongly mixing and ρ-mixing processes.

Original languageEnglish (US)
Pages (from-to)165-179
Number of pages15
JournalScandinavian Journal of Statistics
Volume24
Issue number2
DOIs
StatePublished - Jun 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Asymptotic normality
  • Local polynomial fitting
  • Mixing processes

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