A Novel Nonparametric Multiple Imputation Algorithm for Estimating Missing Data

Citation

Gheyas IA & Smith L (2009) A Novel Nonparametric Multiple Imputation Algorithm for Estimating Missing Data. In: Ao S, Gelman L, Hukins D, Hunter A & Korsunsky A (eds.) Proceedings of The World Congress on Engineering 2009: Volume 2. ICCSDE'09: The 2009 International Conference of Computational Statistics and Data Engineering: London, U.K., 1-3 July, 2009, London, UK, 01.07.2009-03.07.2009. Hong Kong: Newswood Limited, pp. 1281-1286. http://www.iaeng.org/publication/WCE2009/WCE2009_pp1281-1286.pdf

Abstract
The treatment of incomplete data is an important step in pre-processing data prior to later analysis. We propose a novel non-parametric multiple imputation algorithm for estimating missing value. The proposed algorithm is based on Generalized Regression Neural Networks. We compare the proposed algorithm against existing algorithms on forty-five real and synthetic datasets. The effectiveness of imputation algorithms is evaluated in classification problems. The performance of proposed algorithm appears to be superior to that of other algorithms.

Keywords
Missing values; Imputation; Single imputation; Multiple imputation

People

Professor Leslie Smith

Emeritus Professor, Computing Science