TY - JOUR
T1 - Discriminant analysis by quantile regression with application on the climate change problem
AU - Chen, Cathy W.S.
AU - Hsu, Yi Tung
AU - Taniguchi, Masanobu
N1 - Funding Information:
The authors thank the editors and referees for their precious time and valuable comments to improve the quality of this paper. Cathy W.S. Chen's research is funded by the Ministry of Science and Technology, Taiwan (MOST 103-2118-M-035-002-MY2 and MOST 105-2118-M-035-003-MY2). Masanobu Taniguchi's research is supported by JSPS Kiban (A-15H02061) and Houga (26540015).
Publisher Copyright:
© 2017 Elsevier B.V.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/8
Y1 - 2017/8
N2 - 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.
AB - 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.
KW - Classification and discrimination
KW - Misclassification probability
KW - Quantile regression
KW - Time series analysis
KW - Weather data
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U2 - 10.1016/j.jspi.2017.02.002
DO - 10.1016/j.jspi.2017.02.002
M3 - Article
AN - SCOPUS:85014697804
SN - 0378-3758
VL - 187
SP - 17
EP - 27
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
ER -
