Imputing missing values in modelling the PM10 concentrations

Missing values have always been a problem in analysis. Most exclude the missing values from the analyses which may lead to biased parameter estimates. Some imputations methods are considered in this paper in which simulation study is conducted to compare three methods of imputation namely mean subst...

Full description

Bibliographic Details
Main Authors:	Nuradhiathy Abd Razak, Yong Zulina Zubairi, Rossita M. Yunus
Format:	Article
Language:	English
Published:	Universiti Kebangsaan Malaysia 2014
Online Access:	http://journalarticle.ukm.my/7824/ http://journalarticle.ukm.my/7824/ http://journalarticle.ukm.my/7824/1/18_Nuradhiathy.pdf

id	ukm-7824
recordtype	eprints
spelling	ukm-78242016-12-14T06:45:19Z http://journalarticle.ukm.my/7824/ Imputing missing values in modelling the PM10 concentrations Nuradhiathy Abd Razak, Yong Zulina Zubairi, Rossita M. Yunus, Missing values have always been a problem in analysis. Most exclude the missing values from the analyses which may lead to biased parameter estimates. Some imputations methods are considered in this paper in which simulation study is conducted to compare three methods of imputation namely mean substitution, hot deck and expectation maximization (EM) imputation. The EM imputation is found to be superior especially when the percentage of missing values is high as it constantly gives low RMSE as compared with other two methods. The EM imputation method is then applied to the PM10 concentrations data set for the southwest and northeast monsoons in Petaling Jaya and Seberang Perai, Malaysia which has missing values. Four types of distributions, namely the Weibull, lognormal, gamma and Gumbel distribution are considered to describe the PM10 concentrations. The Weibull distribution gives the best fit for the southwest monsoon data for Petaling Jaya. The lognormal distribution outperformed the others in describing the southwest monsoon in Seberang Perai. Meanwhile, for the northeast monsoon in both locations, gamma distribution is the best distribution to describe the data. Universiti Kebangsaan Malaysia 2014-10 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/7824/1/18_Nuradhiathy.pdf Nuradhiathy Abd Razak, and Yong Zulina Zubairi, and Rossita M. Yunus, (2014) Imputing missing values in modelling the PM10 concentrations. Sains Malaysiana, 43 (10). pp. 1599-1607. ISSN 0126-6039 http://www.ukm.my/jsm/
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Missing values have always been a problem in analysis. Most exclude the missing values from the analyses which may lead to biased parameter estimates. Some imputations methods are considered in this paper in which simulation study is conducted to compare three methods of imputation namely mean substitution, hot deck and expectation maximization (EM) imputation. The EM imputation is found to be superior especially when the percentage of missing values is high as it constantly gives low RMSE as compared with other two methods. The EM imputation method is then applied to the PM10 concentrations data set for the southwest and northeast monsoons in Petaling Jaya and Seberang Perai, Malaysia which has missing values. Four types of distributions, namely the Weibull, lognormal, gamma and Gumbel distribution are considered to describe the PM10 concentrations. The Weibull distribution gives the best fit for the southwest monsoon data for Petaling Jaya. The lognormal distribution outperformed the others in describing the southwest monsoon in Seberang Perai. Meanwhile, for the northeast monsoon in both locations, gamma distribution is the best distribution to describe the data.
format	Article
author	Nuradhiathy Abd Razak, Yong Zulina Zubairi, Rossita M. Yunus,
spellingShingle	Nuradhiathy Abd Razak, Yong Zulina Zubairi, Rossita M. Yunus, Imputing missing values in modelling the PM10 concentrations
author_facet	Nuradhiathy Abd Razak, Yong Zulina Zubairi, Rossita M. Yunus,
author_sort	Nuradhiathy Abd Razak,
title	Imputing missing values in modelling the PM10 concentrations
title_short	Imputing missing values in modelling the PM10 concentrations
title_full	Imputing missing values in modelling the PM10 concentrations
title_fullStr	Imputing missing values in modelling the PM10 concentrations
title_full_unstemmed	Imputing missing values in modelling the PM10 concentrations
title_sort	imputing missing values in modelling the pm10 concentrations
publisher	Universiti Kebangsaan Malaysia
publishDate	2014
url	http://journalarticle.ukm.my/7824/ http://journalarticle.ukm.my/7824/ http://journalarticle.ukm.my/7824/1/18_Nuradhiathy.pdf
first_indexed	2023-09-18T19:50:42Z
last_indexed	2023-09-18T19:50:42Z
_version_	1777406199701962752

Imputing missing values in modelling the PM10 concentrations

Similar Items