# The Principal Component Linear Spline Quantile Regression Model in Statistical Downscaling for Rainfall Data
> Yulianti A.S.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_86000476794
Jurnal / Konferensi: Journal of Sciences Islamic Republic of Iran
Tahun terbit: 2024
DOI: https://doi.org/10.22059/jsciences.2024.375343.1007860
ISSN: 10161104
Kuartil SJR: Q4
Citations: 2

## Authors
- Yulianti A.S.

## Abstract
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%.

## Keywords
- Downscaling
- Multicollinearity
- Principal component analysis
- Quantile regression
- Outlier
- Mathematics
- Quantile
- Statistical model
- Statistics
- Linear regression
- Principal component regression
- Nonparametric regression
- Regression analysis
- Linear model
- Spline (mechanical)
- Functional principal component analysis
- Regression
- Econometrics
- Nonparametric statistics
- Smoothing spline
- Grouped data
- Statistical analysis
- Statistical hypothesis testing

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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