Imputation procedures for categorical data: their effects on the goodness-of-fit chi-square statistic
作者:
Phyllis A. Gimotty,
Morton B. Brown,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1990)
卷期:
Volume 19,
issue 2
页码: 681-703
ISSN:0361-0918
年代: 1990
DOI:10.1080/03610919008812882
出版商: Marcel Dekker, Inc.
关键词: missing data;categorical data;resampling plans
数据来源: Taylor
摘要:
An imputation procedure is a procedure by which each missing value in a data set is replaced (imputed) by an observed value using a predetermined resampling procedure. The distribution of a statistic computed from a data set consisting of observed and imputed values, called a completed data set, is affecwd by the imputation procedure used. In a Monte Carlo experiment, three imputation procedures are compared with respect to the empirical behavior of the goodness-of- fit chi-square statistic computed from a completed data set. The results show that each imputation procedure affects the distribution of the goodness-of-fit chi-square statistic in 3. different manner. However, when the empirical behavior of the goodness-of-fit chi-square statistic is compared u, its appropriate asymptotic distribution, there are no substantial differences between these imputation procedures.
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