Efficient estimation of variance components in nonparametric mixed-effects models with large samples

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Abstract

Linear mixed-effects (LME) regression models are a popular approach for analyzing correlated data. Nonparametric extensions of the LME regression model have been proposed, but the heavy computational cost makes these extensions impractical for analyzing large samples. In particular, simultaneous estimation of the variance components and smoothing parameters poses a computational challenge when working with large samples. To overcome this computational burden, we propose a two-stage estimation procedure for fitting nonparametric mixed-effects regression models. Our results reveal that, compared to currently popular approaches, our two-stage approach produces more accurate estimates that can be computed in a fraction of the time.

Original languageEnglish (US)
Pages (from-to)1319-1336
Number of pages18
JournalStatistics and Computing
Volume26
Issue number6
DOIs
StatePublished - 2016

Keywords

  • Algorithms
  • Linear mixed models
  • Nonparametric regression
  • REML
  • Smoothing splines
  • Variance components

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