Manuscript Number : CSEIT183180

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.

  1. Alan Agresti. Categorical Data Analysis (Second Edition). John Wiley and Sons, 2002.
  2. D. Aldous. Exchangeability and Related Topics. In Proceedings of the Ecole d'Ete de Probabilities de Saint-Flour XIII, Pages 1{198. Springer, 1985.
  3. J. A. Anderson. Separate Sample Logistic Discrimination. Biometrika, 59(1):19{35, 1972.
  4. Galen Andrew and Jianfeng Gao. Scalable Training of L1-Regularized Log-Linear Models. In Proceedings of the 24th International Conference on Machine Learning, 2007.
  5. A. Banerjee. An Analysis of Logistic Models: Exponential Family Connections and Online Performance. In SIAM International Conference on Data Mining, 2002.
  6. Halima Bensmail and Gilles Celeux. Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition. Journal of the American Statistical Association, 91(463):1743{1748, 1996.
  7. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
  8. tephen Boyd. An Interior-Point Method for Large-Scale L1-Regularized Logistic Regression. Journal of Machine Learning Research, 8:1519{1555, 2007.
  9. John S. Breese, David Heckerman, and Carl Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Arti_cial Intelligence, Pages 43{52, July 1998.
  10. Christopher J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2):121{167, 1998.
  11. John Canny. Collaborative Filtering with Privacy via Factor Analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 238{245. ACM Press, 2002.
  12. O. Chapelle, B. Scholkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
  13. Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, and Daphne Koller. Max-Margin Classi_cation of Incomplete Data. In Advances in Neural Information Processing Systems 19, 2006.
  14. Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, and Daphne Koller. Max-Margin Classi_cation of Data with Absent Features. Journal of Machine Learning Research, 9:1{27, 2007.
  15. D. R. Cox and E. J. Snell. Analysis of Binary Data. Chapman Hall, second edition, 1989.
  16. Dennis Decoste. Collaborative Prediction Using Ensembles of Maximum Margin Matrix Factorizations. In Proceedings of the 23rd International Conference on Machine Learning, Pages 249{256, 2006.
  17. A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum Likelihood From Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1{38, 1977.
  18. M.Ramaraj, Dr.S.Niraimathi Application of color based image segmentation paradigm on RGB Color pixels using fuzzy c-means and k-means algorithms in International Journal of Computer Science and Mobile computing, ISSN- 2320-088X

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. |          | BibTeX | RIS | CSV

Article Preview