Discriminant analysis by quantile regression with application on the climate change problem

Cathy W.S. Chen*, Yi Tung Hsu, Masanobu Taniguchi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


With the widespread use of discriminant analysis in various fields, e.g. multivariate data, regression models, and times series observations, this paper introduces a quantile regression statistic to classify time series data into a certain category. Results show that the misclassification probability of the discriminant statistic converges to zero as the sample size tends to infinity. We also evaluate the performance of the statistics when the categories are contiguous. We apply the proposed method in quantile autoregression to a dataset of the monthly mean maximum temperature at Melbourne, Australia from January 1944 to December 2015. The findings illuminate interesting features of climate change and allow us to check the change at each quantile of the innovation distribution. Because the proposed method is general, there are many potential applications of this approach.

Original languageEnglish
Pages (from-to)17-27
Number of pages11
JournalJournal of Statistical Planning and Inference
Publication statusPublished - 2017 Aug


  • Classification and discrimination
  • Misclassification probability
  • Quantile regression
  • Time series analysis
  • Weather data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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