Sufficient dimension reduction for longitudinal data

Xuan Bi, Annie Qu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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
Issue number2
StatePublished - Apr 2015
Externally publishedYes

Bibliographical note

Funding Information:
The authors are very grateful to the Co-Editor, two referees and an associate editor for their insightful comments and suggestions that have significantly improved the manuscript. The authors are also grateful to Professor Lexin Li for stimulating discussion on this topic. The research was supported by a National Science Foundation Grant (DMS-0906660 and DMS-1308227).


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


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