Efficient classification for longitudinal data

Xianlong Wang, Annie Qu

Research output: Contribution to journalArticlepeer-review

Abstract

A new classifier, QIFC, is proposed based on the quadratic inference function for longitudinal data. Our approach builds a classifier by taking advantage of modeling information between the longitudinal responses and covariates for each class, and assigns a new subject to the class with the shortest newly defined distance to the subject. For finite sample applications, this enables one to overcome the difficulty in estimating covariance matrices while still incorporating correlation into the classifier. The proposed classifier only requires the first moment condition of the model distribution, and hence is able to handle both continuous and discrete responses. Simulation studies show that QIFC outperforms competing classifiers, such as the functional data classifier, support vector machine, logistic regression, linear discriminant analysis, the naive Bayes classifier and the decision tree in various practical settings. Two time-course gene expression data sets are used to assess the performance of QIFC in applications.

Original languageEnglish (US)
Pages (from-to)119-134
Number of pages16
JournalComputational Statistics and Data Analysis
Volume78
DOIs
StatePublished - Oct 2014

Keywords

  • Classification
  • Linear discriminant analysis
  • Longitudinal data analysis
  • QIFC
  • Quadratic distance
  • Quadratic inference function

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Efficient classification for longitudinal data'. Together they form a unique fingerprint.

Cite this