Missing Value Estimation Methods for Data in Linear Functional Relationship Model

Ghapor, Adilah Abdul; Zubairi, Yong Zulina; Imon, A. H. M. Rahmatullah

doi:10.17576/jsm-2017-4602-17

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Because it considers the presence of error across all parameters, EIVM is the most statistically relevant tool for predicting reactivity ratios. EIVM is classified into three types: functional, structural, and ultrastructural (Ghapor et al, 2014;Jamaliyatul et al, 2023). A functional relationship model between X and Y is when X is a mathematical variable or fixed constant.…”

Section: Introductionmentioning

confidence: 99%

Modelling Wind Speed, Humidity, and Temperature in Butterworth and Melaka during Southwest Monsoon in 2020 with a Simultaneous Linear Functional Relationship

Jamaliyatul,

Mokhtar,

Badyalina

et al. 2024

Mal. J. Fund. Appl. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%