Abstract
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 language | English |
|---|---|
| Journal | Journal of Statistical Planning and Inference |
| DOIs | |
| Publication status | Accepted/In press - 2016 Apr 5 |
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Keywords
- 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
Cite this
Discriminant analysis by quantile regression with application on the climate change problem. / Chen, Cathy W S; Hsu, Yi Tung; Taniguchi, Masanobu.
In: Journal of Statistical Planning and Inference, 05.04.2016.Research output: Contribution to journal › Article
}
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
PY - 2016/4/5
Y1 - 2016/4/5
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
UR - http://www.scopus.com/inward/record.url?scp=85014697804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014697804&partnerID=8YFLogxK
U2 - 10.1016/j.jspi.2017.02.002
DO - 10.1016/j.jspi.2017.02.002
M3 - Article
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
SN - 0378-3758
ER -