Smoothed functional canonical correlation analysis of humidity and temperature data


Köymen Keser İ., Deveci Kocakoç İ.

JOURNAL OF APPLIED STATISTICS, cilt.42, sa.10, ss.2126-2140, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 10
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1080/02664763.2015.1019842
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2126-2140
  • Anahtar Kelimeler: functional data analysis, smoothing, functional canonical correlation analysis, meteorological data
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper focuses on smoothed functional canonical correlation analysis (SFCCA) to investigate the relationships and changes in large, seasonal and long-term data sets. The aim of this study is to introduce a guideline for SFCCA for functional data and to give some insights on the fine tuning of the methodology for long-term periodical data. The guidelines are applied on temperature and humidity data for 11 years between 2000 and 2010 and the results are interpreted. Seasonal changes or periodical shifts are visually studied by yearly comparisons. The effects of the number of basis functions' and the selection of smoothing parameter' on the general variability structure and on correlations between the curves are examined. It is concluded that the number of time points (knots), number of basis functions and the time span of evaluation (monthly, daily, etc.) should all be chosen harmoniously. It is found that changing the smoothing parameter does not have a significant effect on the structure of curves and correlations. The number of basis functions is found to be the main effector on both individual and correlation weight functions.