Unified inference for sparse and dense longitudinal models

Seonjin Kim, Zhibiao Zhao

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In longitudinal data analysis, statistical inference for sparse data and dense data could be substantially different. For kernel smoothing, the estimate of the mean function, the convergence rates and the limiting variance functions are different in the two scenarios. This phenomenon poses challenges for statistical inference, as a subjective choice between the sparse and dense cases may lead to wrong conclusions. We develop methods based on self-normalization that can adapt to the sparse and dense cases in a unified framework. Simulations show that the proposed methods outperform some existing methods.

Original languageEnglish (US)
Pages (from-to)203-212
Number of pages10
JournalBiometrika
Volume100
Issue number1
DOIs
StatePublished - Mar 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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