期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 51, 期 12, 页码 5913-5917出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2006.11.007
关键词
hot deck; imputation; survey data; k-nearest-neighbour; convergence
Random k-nearest-neighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missingness is independent of unobserved values (MAR), and assume there is a minimum positive probability of a response vector being complete. Then RKNN, with k equal to the square root of the sample size, asymptotically produces independent values with the correct probability distribution for the ones that are missing. An experiment illustrates two different distance functions for a synthetic data set. (C) 2006 Elsevier B.V. All rights reserved.
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