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The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia)
Version 1
: Received: 13 October 2018 / Approved: 15 October 2018 / Online: 15 October 2018 (11:09:48 CEST)
How to cite:
Astutik, S.; Sa'adah, U.; Adhisuwignjo, S.; Sumara, R. The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia). Preprints2018, 2018100302. https://doi.org/10.20944/preprints201810.0302.v1
Astutik, S.; Sa'adah, U.; Adhisuwignjo, S.; Sumara, R. The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia). Preprints 2018, 2018100302. https://doi.org/10.20944/preprints201810.0302.v1
Astutik, S.; Sa'adah, U.; Adhisuwignjo, S.; Sumara, R. The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia). Preprints2018, 2018100302. https://doi.org/10.20944/preprints201810.0302.v1
APA Style
Astutik, S., Sa'adah, U., Adhisuwignjo, S., & Sumara, R. (2018). The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia). Preprints. https://doi.org/10.20944/preprints201810.0302.v1
Chicago/Turabian Style
Astutik, S., Supriatna Adhisuwignjo and Rauzan Sumara. 2018 "The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia)" Preprints. https://doi.org/10.20944/preprints201810.0302.v1
Abstract
The hourly and daily rainfall data which is spatially distributed are required as an input for run-off rain model. Furthermore, the run-off rain model is used to detect early flooding. The daily and hourly rainfall data have characteristics that most of data are zero. Therefore we need a model which can capture the phenomenon. A time series model involving location, which is a model that can be developed to approach the daily and hourly rainfall data, we can call Vector Autoregressive (VAR) model. The VAR model allows us for modeling rainfall data in several areas. However, in certain conditions the VAR model often occurs over-parameterization and reduces degrees of freedom. The aim of this study is to compare the VAR model with Maximum Likelihood Estimator (MLE) and Bayesian to hourly and daily rainfall data in SampeanWatershed of Bondowoso. The results showed that the hourly and daily rainfall data are fitted to VAR process of orde 5 and 1 respectively. Based on the AIC and SBC values indicate that the Bayesian is better than the MLE method. The Bayesian is able to predict parameters by producing a smaller variance covariance matrix than the MLE.
Keywords
VAR; MLE; Bayesian
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
