Cover Image

Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution

Organized by Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society, 5th - 6th December 2007 at The Gurney Hotel, Penang.

Saved in:
Bibliographic Details
Main Authors: Norazian, Mohamed Noor, Mohd Mustafa, Al Bakri Abdullah, Ahmad Shukri, Yahaya, Nor Azam, Ramli
Format: Working Paper
Language:English
Published: Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society 2008
Subjects:
Missing values
Goodness-of-fit
Gamma distribution
Interpolation
Estimation theory
Linear interpolation
Air pollution
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/1897
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-1897
record_format dspace
spelling my.unimap-18972009-04-16T03:26:25Z Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution Norazian, Mohamed Noor Mohd Mustafa, Al Bakri Abdullah Ahmad Shukri, Yahaya Nor Azam, Ramli Missing values Goodness-of-fit Gamma distribution Interpolation Estimation theory Linear interpolation Air pollution Organized by Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society, 5th - 6th December 2007 at The Gurney Hotel, Penang. The presence of missing values in statistical survey data is an important issue to deal with. These data usually contained missing values due to many factors such as machine failures, changes in the siting monitors, routine maintenance and human error. Incomplete data set usually cause bias due to differences between observed and unobserved data. Therefore, it is important to ensure that the data analyzed are of high quality. A straightforward approach to deal with this problem is to ignore the missing data and to discard those incomplete cases from the data set. This approach is generally not valid for time-series prediction, in which the value of a system typically depends on the historical time data of the system. One approach that commonly used for the treatment of this missing item is adoption of imputation technique. This paper discusses three interpolation methods that are linear, quadratic and cubic. A total of 8577 observations of PM10 data for a year were used to compare between the three methods when fitting the Gamma distribution. The goodness-of-fit were obtained using three performance indicators that are mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2). The results shows that the linear interpolation method provides a very good fit to the data. 2008-09-02T06:12:31Z 2008-09-02T06:12:31Z 2007-12-06 Working Paper http://hdl.handle.net/123456789/1897 en The 3rd IMT-GT 2007 Regional Conference on Mathemathics, Statistics and Applications (IMT-GT RCMSA 2007) Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Missing values
Goodness-of-fit
Gamma distribution
Interpolation
Estimation theory
Linear interpolation
Air pollution
spellingShingle Missing values
Goodness-of-fit
Gamma distribution
Interpolation
Estimation theory
Linear interpolation
Air pollution
Norazian, Mohamed Noor
Mohd Mustafa, Al Bakri Abdullah
Ahmad Shukri, Yahaya
Nor Azam, Ramli
Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
description Organized by Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society, 5th - 6th December 2007 at The Gurney Hotel, Penang.
format Working Paper
author Norazian, Mohamed Noor
Mohd Mustafa, Al Bakri Abdullah
Ahmad Shukri, Yahaya
Nor Azam, Ramli
author_facet Norazian, Mohamed Noor
Mohd Mustafa, Al Bakri Abdullah
Ahmad Shukri, Yahaya
Nor Azam, Ramli
author_sort Norazian, Mohamed Noor
title Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
title_short Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
title_full Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
title_fullStr Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
title_full_unstemmed Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
title_sort estimating missing data in air pollution data using interpolation technique: effects on fitting gamma distribution
publisher Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society
publishDate 2008
url http://dspace.unimap.edu.my/xmlui/handle/123456789/1897
_version_ 1643787495350992896
score 13.222552

Similar Items