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dc.contributor.authorNorazian, Mohamed Noor
dc.contributor.authorShukri, Yahaya Ahmad
dc.contributor.authorAzam, Ramli Nor
dc.contributor.authorMohd. Mustafa Al Bakri, Abdullah
dc.date.accessioned2009-07-29T08:18:19Z
dc.date.available2009-07-29T08:18:19Z
dc.date.issued2008
dc.identifier.citationScienceAsia, vol. 34 (2), 2008 pages 341-345.en_US
dc.identifier.issn1513-1874
dc.identifier.uri10.2306/scienceasia1513-1874.2008.34.341
dc.identifier.urihttp://www.scienceasia.org/content/content.php?v=34&i=3&m=9&y=2008
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6612
dc.descriptionLink to publisher's homepage at http://www.scienceasia.orgen_US
dc.description.abstractAir pollution data obtained using automated machines often contain missing values which can cause bias due to systematic differences between observed and unobserved data. We used interpolation and mean imputation techniques to replace simulated missing values from annual hourly monitoring data for PM10. The most effective method for generating the missing data points was to replace each missing value with the mean of the two data points before and after the missing value. This approach was referred to as the mean-before-after method.en_US
dc.language.isoenen_US
dc.publisherScience Society of Thailanden_US
dc.subjectAir pollutionen_US
dc.subjectImputationen_US
dc.subjectPerformance indicatorsen_US
dc.subjectMissing valuesen_US
dc.subjectEstimation theoryen_US
dc.subjectLinear interpolationen_US
dc.subjectInterpolationen_US
dc.titleEstimation of missing values in air pollution data using single imputation techniquesen_US
dc.typeArticleen_US


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