Sieve extremum estimates for weakly dependent data

Xiaohong Chen, Xiaotong Shen

Research output: Contribution to journalArticlepeer-review

127 Scopus citations


Many non/semi-parametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary β-mixing observations, we obtain convergence rates of sieve extremum estimates and root-n asymptotic normality of "plug-in" sieve extremum estimates of smooth functionals. As applications to time series models, we give convergence rates for nonparametric ARX(p, q) regression via neural networks, splines, and wavelets; root-n asymptotic normality for partial linear additive AR(p) models, and monotone transformation AR(1) models.

Original languageEnglish (US)
Pages (from-to)289-314
Number of pages26
Issue number2
StatePublished - Mar 1998


  • Neural networks
  • Rate and normality
  • Shape-preserving splines
  • Sieve extremum estimates
  • Wavelets
  • β-mixing


Dive into the research topics of 'Sieve extremum estimates for weakly dependent data'. Together they form a unique fingerprint.

Cite this