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...

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Published in:MALAYSIAN JOURNAL OF FUNDAMENTAL AND APPLIED SCIENCES
Main Authors: Jamaliyatul, Nur Ain Al-Hameefatul; Mokhtar, Nurkhairany Amyra; Badyalina, Basri; Rambli, Adzhar; Zubairi, Yong Zulina
Format: Article
Language:English
Published: PENERBIT UTM PRESS 2024
Subjects:
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
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)
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