The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data
Information regarding rainfall can be obtained from global data, namely the global climate model that can be accessed through the statistical downscaling approach. Linear spline quantile regression with principal component is a statistical method that can be employed in statistical downscaling to ad...
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| Format: | Article |
| Language: | English |
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University of Tehran
2024-04-01
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| Series: | Journal of Sciences, Islamic Republic of Iran |
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| Online Access: | https://jsciences.ut.ac.ir/article_98199_9e08334e8c262046ad384e0f6acb3c1b.pdf |
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| author | Andi Yulianti Anna Islamiyati Erna Herdiani |
| author_facet | Andi Yulianti Anna Islamiyati Erna Herdiani |
| author_sort | Andi Yulianti |
| collection | DOAJ |
| description | Information regarding rainfall can be obtained from global data, namely the global climate model that can be accessed through the statistical downscaling approach. Linear spline quantile regression with principal component is a statistical method that can be employed in statistical downscaling to address multicollinearity and outliers in data by using nonparametric estimators. This method is applied to rainfall data in Pangkep Regency from January 2008 to December 2022 as the response variable and global climate model data as the predictor variable. The aim of this research is to obtain the best regression model used for predicting rainfall data. The results obtained indicate that statistical downscaling with two principal components at the 0.50 quantile with respective knot points of -10.20 and -0.30 is the best model with the lowest generalized cross-validation value. The forecasted rainfall data using this model shows a high level of accuracy with a correlation of 89%. |
| format | Article |
| id | doaj-art-b329d618bb754a4d8ca6c1eb5d335767 |
| institution | OA Journals |
| issn | 1016-1104 2345-6914 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Sciences, Islamic Republic of Iran |
| spelling | doaj-art-b329d618bb754a4d8ca6c1eb5d3357672025-08-20T02:25:54ZengUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-11042345-69142024-04-0135215916610.22059/jsciences.2024.375343.100786098199The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall DataAndi Yulianti0Anna Islamiyati1Erna Herdiani2Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, IndonesiaInformation regarding rainfall can be obtained from global data, namely the global climate model that can be accessed through the statistical downscaling approach. Linear spline quantile regression with principal component is a statistical method that can be employed in statistical downscaling to address multicollinearity and outliers in data by using nonparametric estimators. This method is applied to rainfall data in Pangkep Regency from January 2008 to December 2022 as the response variable and global climate model data as the predictor variable. The aim of this research is to obtain the best regression model used for predicting rainfall data. The results obtained indicate that statistical downscaling with two principal components at the 0.50 quantile with respective knot points of -10.20 and -0.30 is the best model with the lowest generalized cross-validation value. The forecasted rainfall data using this model shows a high level of accuracy with a correlation of 89%.https://jsciences.ut.ac.ir/article_98199_9e08334e8c262046ad384e0f6acb3c1b.pdfprincipal componentquantile regressionrainfallsplinestatistical downscaling |
| spellingShingle | Andi Yulianti Anna Islamiyati Erna Herdiani The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data Journal of Sciences, Islamic Republic of Iran principal component quantile regression rainfall spline statistical downscaling |
| title | The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data |
| title_full | The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data |
| title_fullStr | The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data |
| title_full_unstemmed | The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data |
| title_short | The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data |
| title_sort | principal component linear spline quantile regression model in statistical downscaling for rainfall data |
| topic | principal component quantile regression rainfall spline statistical downscaling |
| url | https://jsciences.ut.ac.ir/article_98199_9e08334e8c262046ad384e0f6acb3c1b.pdf |
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