TY  - JOUR
TI  - Multiple Imputation for Missing Data Using Factored Regression Modelwith the Implementation of Current Population 
AU  - S. Dilip Kumar
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2018/02/28
PY  - 2018
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT183180
VL  - 3
IS  - 1
SP  - 549	
EP  - 555
AB  - Missing value or data is a major issue in all fields. Many models and methods are supported to substitute the missing values. In this paper, we promote the use of statistical methods for treating missing data that employ single- or multiple- imputation of missing values. Proposed a method, called factored regression model to multiply impute missing values in such data sets by modelling the joint distribution of the variables in the data through a sequence of generalised linear models. Apply our model to protect confidentiality of the current population survey data by generating multiply imputed, partially synthetic data sets.