On the Use of Sequential Adaptive Nearest Neighbors for Missing Value Imputation Park, So-Hyun; Bang, Sung-Wan; Jhun, Myoung-Shic;
In this paper, we propose a Sequential Adaptive Nearest Neighbor(SANN) imputation method that combines the Adaptive Nearest Neighbor(ANN) method and the Sequential k-Nearest Neighbor(SKNN) method. When choosing the nearest neighbors of missing observations, the proposed SANN method takes the local feature of the missing observations into account as well as reutilizes the imputed observations in a sequential manner. By using a Monte Carlo study and a real data example, we demonstrate the characteristics of the SANN method and its potential performance.
Dixon, J. K. (1979). Pattern recognition with partly missing data, IEEE Transactions on Systems, Man, and Cybernetics, 9, 617-621.
Jhun, M., Jeong, H. C. and Koo, J. Y. (2007). On the use of adaptive nearest neighbors for missing value imputation, Communications in Statistics: Simulation and Computation, 36, 1275-1286.
Kim, K. Y., Kim, B. J. and Yi, G. S. (2004). Reuse of imputed data in microarray analysis increases imputation efficiency, BMC Bioinformatics, 5, 160.
Little, R. J. A. and Rubin, D. B. (1987). Statistical Analysis With Missing Data, Wiley, New York.
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D. and Altman, R. B. (2001). Missing value estimation methods for DNA microarrays, Bioinformatics, 7, 520-525.