Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship
The extension of parameter estimation from a bivariate linear functional relationship model (LFRM) to simultaneous LFRM for linear variables using the maximum likelihood estimation (MLE) method is explored in this paper. The covariance matrix of the parameter estimates is derived through the Fisher...
Published in: | MALAYSIAN JOURNAL OF FUNDAMENTAL AND APPLIED SCIENCES |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
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PENERBIT UTM PRESS
2024
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001221789500008 |
author |
Jamaliyatul Nur Ain Al-Hameefatul; Mokhtar Nurkhairany Amyra; Badyalina Basri; Rambli Adzhar; Zubairi Yong Zulina |
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spellingShingle |
Jamaliyatul Nur Ain Al-Hameefatul; Mokhtar Nurkhairany Amyra; Badyalina Basri; Rambli Adzhar; Zubairi Yong Zulina Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship Science & Technology - Other Topics |
author_facet |
Jamaliyatul Nur Ain Al-Hameefatul; Mokhtar Nurkhairany Amyra; Badyalina Basri; Rambli Adzhar; Zubairi Yong Zulina |
author_sort |
Jamaliyatul |
spelling |
Jamaliyatul, Nur Ain Al-Hameefatul; Mokhtar, Nurkhairany Amyra; Badyalina, Basri; Rambli, Adzhar; Zubairi, Yong Zulina Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship MALAYSIAN JOURNAL OF FUNDAMENTAL AND APPLIED SCIENCES English Article The extension of parameter estimation from a bivariate linear functional relationship model (LFRM) to simultaneous LFRM for linear variables using the maximum likelihood estimation (MLE) method is explored in this paper. The covariance matrix of the parameter estimates is derived through the Fisher information matrix. A simulation study was done to investigate the performance of the parameter estimation. According to the simulation study, the estimated parameters have a small bias. The beauty of simultaneous LFRM lies in developing the model to study the relationship between more than two linear variables while considering error terms for all variables. The applicability of the proposed simultaneous model is demonstrated using wind speed, humidity, and temperature data from Butterworth and Melaka during the southwest monsoon season of 2020. PENERBIT UTM PRESS 2289-5981 2289-599X 2024 20 2 10.11113/mjfas.v20n2.3342 Science & Technology - Other Topics gold WOS:001221789500008 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001221789500008 |
title |
Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship |
title_short |
Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship |
title_full |
Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship |
title_fullStr |
Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship |
title_full_unstemmed |
Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship |
title_sort |
Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship |
container_title |
MALAYSIAN JOURNAL OF FUNDAMENTAL AND APPLIED SCIENCES |
language |
English |
format |
Article |
description |
The extension of parameter estimation from a bivariate linear functional relationship model (LFRM) to simultaneous LFRM for linear variables using the maximum likelihood estimation (MLE) method is explored in this paper. The covariance matrix of the parameter estimates is derived through the Fisher information matrix. A simulation study was done to investigate the performance of the parameter estimation. According to the simulation study, the estimated parameters have a small bias. The beauty of simultaneous LFRM lies in developing the model to study the relationship between more than two linear variables while considering error terms for all variables. The applicability of the proposed simultaneous model is demonstrated using wind speed, humidity, and temperature data from Butterworth and Melaka during the southwest monsoon season of 2020. |
publisher |
PENERBIT UTM PRESS |
issn |
2289-5981 2289-599X |
publishDate |
2024 |
container_volume |
20 |
container_issue |
2 |
doi_str_mv |
10.11113/mjfas.v20n2.3342 |
topic |
Science & Technology - Other Topics |
topic_facet |
Science & Technology - Other Topics |
accesstype |
gold |
id |
WOS:001221789500008 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001221789500008 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809679004404285440 |