Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall
Science Journal of Applied Mathematics and Statistics
Volume 2, Issue 3, June 2014, Pages: 66-70
Received: May 30, 2014; Accepted: Jun. 30, 2014; Published: Jul. 20, 2014
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Aji Hamim Wigena, Department of Statistics, Bogor Agricultural University, Bogor, Indonesia
Anik Djuraidah, Department of Statistics, Bogor Agricultural University, Bogor, Indonesia
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Extreme rainfall events have been great interest in statistical downscaling. This paper concerns with developing model of statistical downscaling using quantile regression to estimate extreme monthly rainfall. Statistical downscaling relates functionally local scale response variable and global scale predictor variables. The response variable is monthly rainfall from 1979 to 2008 at station Bangkir Indonesia and the predictor variables are monthly precipitation of 64 grid of Global Circulation Model output in the same period. Principal Component Analysis is used to reduce dimension of predictors. A number of components for developing quantile regression model are determined based on Quantile Verification Skill Score. The results show that at 95th quantile the pattern of forecasted rainfall in January to December 2008 is similar to actual rainfall with correlation 0.98 and the forecasted rainfall (843 mm) in February 2008 is considered as the extreme rainfall which confirms well to the highest actual rainfall (727 mm) with probability 0.99.
Collinear, Extreme, Principal Component Analysis, Statistical Downscaling, Quantile Regression, Logistic Regression
To cite this article
Aji Hamim Wigena, Anik Djuraidah, Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall, Science Journal of Applied Mathematics and Statistics. Vol. 2, No. 3, 2014, pp. 66-70. doi: 10.11648/j.sjams.20140203.12
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