Browsing by Subject "Estimation theory"
Now showing items 1-5 of 5
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A comparative study of missing value estimation methods: which method performs better?
(Institute of Electrical and Electronics Engineering (IEEE), 2008-12-01)Missing data is a problem that permeates much of the research bring done today. Some data frequently contain missing values such as gene expression data, which most of its down stream analyses for microarray experiments ... -
Estimating missing data in air pollution data using interpolation technique: effects on fitting Gamma Distribution
(Universiti Sains Malaysia (USM) & Malaysian Mathematical Sciences Society, 2007-12-06)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 ... -
Estimation of missing values for air pollution data using Interpolation technique
(Universiti Malaysia Perlis, 2006)Air pollution data such as PM10, sulphur dioxide, ozone and carbon monoxide are usually obtained using automated machines located at different sites. These are usually due to mechanical failure, routine maintenance, changes ... -
Estimation of missing values in air pollution data using single imputation techniques
(Science Society of Thailand, 2008)Air 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 ... -
The Replacement of Missing Values of Continous Air Pollution Monitoring Data using Mean Top Bottom Imputation technique
(Kolej Universiti Kejuruteraan Utara Malaysia, 2006)Air pollutants data such as PM10 carbon monoxide, sulphur dioxide and ozone concentration were obtained from automated monitoring stations. These data usually contain missing values that can cause bias due to systematic ...