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Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)

Model selection is the process of choosing a model from a set of possible models. The model's ability to generalise means it can fit both current and future data. Despite numerous emergences of procedures in selecting models automatically, there has been a lack of studies on procedures in selec...

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Main Author: Kamarudin, Nur Azulia
Format: Monograph
Language:English
Published: UUM 2021
Subjects:
QA Mathematics
Online Access:https://repo.uum.edu.my/id/eprint/31652/1/14925.pdf
https://repo.uum.edu.my/id/eprint/31652/
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spelling my.uum.repo.316522024-12-09T12:58:07Z https://repo.uum.edu.my/id/eprint/31652/ Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925) Kamarudin, Nur Azulia QA Mathematics Model selection is the process of choosing a model from a set of possible models. The model's ability to generalise means it can fit both current and future data. Despite numerous emergences of procedures in selecting models automatically, there has been a lack of studies on procedures in selecting multiple equations models, particularly seemingly unrelated regression equations (SURE) models. Hence, this study concentrates on an automated model selection procedure for the SURE model by integrating the expectation-maximization (EM) algorithm estimation method, named SURE(EM)-Autometrics. This extension procedure was originally initiated from Autometrics, which is only applicable for a single equation. Simulation analysis was conducted under two strengths of correlation among equations and two levels of significance for a two-equation model with up to 18 variables in the initial general unrestricted model (GUM). Three econometric models have been utilised as a testbed for true specification search. The results were divided into four categories where a tight significance level of 1% had contributed a high percentage of all equations in the model contain variables precisely comparable to the true specifications. Then, an empirical comparison of four model selection techniques was conducted using national growth rates and water quality index (WQI) data. System selection to select all equations in the model simultaneously proved to be more efficient than single equation selection. SURE(EM)-Autometrics dominated the comparison by being at the top of the rankings for most of the error measures. Hence, the integration of EM algorithm estimation is applicable in improving the performance of automated model selection procedures for multiple equations models UUM 2021 Monograph NonPeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/31652/1/14925.pdf Kamarudin, Nur Azulia (2021) Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925). Project Report. UUM. (Submitted)
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Kamarudin, Nur Azulia
Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
description Model selection is the process of choosing a model from a set of possible models. The model's ability to generalise means it can fit both current and future data. Despite numerous emergences of procedures in selecting models automatically, there has been a lack of studies on procedures in selecting multiple equations models, particularly seemingly unrelated regression equations (SURE) models. Hence, this study concentrates on an automated model selection procedure for the SURE model by integrating the expectation-maximization (EM) algorithm estimation method, named SURE(EM)-Autometrics. This extension procedure was originally initiated from Autometrics, which is only applicable for a single equation. Simulation analysis was conducted under two strengths of correlation among equations and two levels of significance for a two-equation model with up to 18 variables in the initial general unrestricted model (GUM). Three econometric models have been utilised as a testbed for true specification search. The results were divided into four categories where a tight significance level of 1% had contributed a high percentage of all equations in the model contain variables precisely comparable to the true specifications. Then, an empirical comparison of four model selection techniques was conducted using national growth rates and water quality index (WQI) data. System selection to select all equations in the model simultaneously proved to be more efficient than single equation selection. SURE(EM)-Autometrics dominated the comparison by being at the top of the rankings for most of the error measures. Hence, the integration of EM algorithm estimation is applicable in improving the performance of automated model selection procedures for multiple equations models
format Monograph
author Kamarudin, Nur Azulia
author_facet Kamarudin, Nur Azulia
author_sort Kamarudin, Nur Azulia
title Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
title_short Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
title_full Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
title_fullStr Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
title_full_unstemmed Sure (EM)-Autometrics: An Automated Model Selection Procedure with Expectation Maximization Algorithm Estimation Method (S/O 14925)
title_sort sure (em)-autometrics: an automated model selection procedure with expectation maximization algorithm estimation method (s/o 14925)
publisher UUM
publishDate 2021
url https://repo.uum.edu.my/id/eprint/31652/1/14925.pdf
https://repo.uum.edu.my/id/eprint/31652/
_version_ 1818837608899280896
score 13.244413

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