clusterwise regression
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2019 ◽  
Vol 3 (3) ◽  
pp. 236-246
Author(s):  
Victor Pandapotan Butar-butar ◽  
Agus M Soleh ◽  
Aji H Wigena

Statistical downscaling (SDS) is one of the developing models for rainfall estimation. The SDS model is a regression model used to analyze the relation of global (GCM output) and local data (rainfall). Rainfall has large variance so that clustering is needed to minimize the variance. One of the analytical methods that can be used in clustering rainfall estimation is cluster wise regression. There are three Methods for Clusterwise regression namely Linear Regresion, Finite Mixture Method (FMM) and Cluster-Weighted Method (CWM). This study used GCM outputs data namely CFRSv2 as a covariate. The response variable is rainfall data in four stations such as Bandung, Bogor, Citeko and Jatiwangi from BMKG. The purpose of this study is to increase the accuracy of rainfall estimation using the three methods and compare the clusterwise regression with PCR and PLS models. Based on the value of RMSEP, the clusterwise regression with FMM was the best method to estimate rainfall in four stations.



Author(s):  
Rodrigo Rivera-Castro ◽  
Aleksandr Pletnev ◽  
Polina Pilyugina ◽  
Grecia Diaz ◽  
Ivan Nazarov ◽  
...  


Author(s):  
Naveen Veeramisti ◽  
Alexander Paz ◽  
Mukesh Khadka ◽  
Cristian Arteaga




Author(s):  
Luis Angel García-Escudero ◽  
Alfonso Gordaliza ◽  
Francesca Greselin ◽  
Agustín Mayo-Iscar


2018 ◽  
Vol 144 ◽  
pp. 239-250
Author(s):  
Beck Gaël ◽  
Azzag Hanane ◽  
Bougeard Stéphanie ◽  
Lebbah Mustapha ◽  
Niang Ndèye


Psychometrika ◽  
2016 ◽  
Vol 82 (1) ◽  
pp. 86-111 ◽  
Author(s):  
Tom Frans Wilderjans ◽  
Eva Vande Gaer ◽  
Henk A. L. Kiers ◽  
Iven Van Mechelen ◽  
Eva Ceulemans


2015 ◽  
Vol 30 (6) ◽  
pp. 3045-3052 ◽  
Author(s):  
Ran Li ◽  
Chenghong Gu ◽  
Furong Li ◽  
Gavin Shaddick ◽  
Mark Dale


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