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

Cathy W S Chen, Yi Tung Hsu, Masanobu Taniguchi

    Research output: Contribution to journalArticlepeer-review


    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
    JournalJournal of Statistical Planning and Inference
    Publication statusAccepted/In press - 2016 Apr 5


    • 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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