Missing data are often encountered in many areas of research. Complete case analysis and indicator method can lead to serious bias. One of the comforting methods is implementation of imputation methods. The main purpose ...

Almost all real life datasets consist missing values. These are usually due to machine failure, routine maintenance, changes in siting monitors and human error. The occurence of missing values requires special attention ...

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 ...