Sufficient dimension reduction for longitudinal data

Xuan Bi, Annie Qu

Research output: Contribution to journalArticlepeer-review

Abstract

Correlation structure contains important information about longitudinal data. Existing sufficient dimension reduction approaches assuming independence may lead to substantial loss of efficiency. We apply the quadratic inference function to incorporate the correlation information and apply the transformation method to recover the central subspace. The proposed estimators are shown to be consistent and more efficient than the ones assuming independence. In addition, the estimated central subspace is also efficient when the correlation information is taken into account. We compare the proposed method with other dimension reduction approaches through simulation studies, and apply this new approach to longitudinal data for an environmental health study.

Original languageEnglish (US)
Pages (from-to)787-807
Number of pages21
JournalStatistica Sinica
Volume25
Issue number2
DOIs
StatePublished - Apr 2015

Keywords

  • Correlation structure
  • Eigen-decomposition
  • Quadratic inference function
  • Slice inverse regression
  • Transformation method

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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