Probability-enhanced effective dimension reduction for classifying sparse functional data

• Fang Yao ^[1] ; Yichao Wu ^[2] ; Jialin Zou ^[1]
  1. [1] University of Toronto

     University of Toronto

     Canadá

     
  2. [2] North Carolina State University

     North Carolina State University

     Township of Raleigh, Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 25, Nº. 1, 2016, págs. 1-22
• Idioma: inglés
• DOI: 10.1007/s11749-015-0470-2
• Texto completo no disponible (Saber más ...)
• Resumen

  • We consider the classification of sparse functional data that are often encountered in longitudinal studies and other scientific experiments. To utilize the information from not only the functional trajectories but also the observed class labels, we propose a probability-enhanced method achieved by weighted support vector machine based on its Fisher consistency property to estimate the effective dimension reduction space. Since only a few measurements are available for some, even all, individuals, a cumulative slicing approach is suggested to borrow information across individuals. We provide justification for validity of the probability-based effective dimension reduction space, and a straightforward implementation that yields a low-dimensional projection space ready for applying standard classifiers. The empirical performance is illustrated through simulated and real examples, particularly in contrast to classification results based on the prominent functional principal component analysis.