Multiple Imputation for Missing Data Using Factored Regression Modelwith the Implementation of Current Population

Authors(1) :-S. Dilip Kumar

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.

Authors and Affiliations

S. Dilip Kumar
MCA.,M.Phil, Assistant professor, Department of Computer Applications, NGM College, pollachi, Tamil Nadu, India

Data mining, Missing Values, Multiple Imputation, Factored Regression.

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 549-555
Manuscript Number : CSEIT183180
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

S. Dilip Kumar, "Multiple Imputation for Missing Data Using Factored Regression Modelwith the Implementation of Current Population ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.549-555, January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT183180

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