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http://dx.doi.org/10.5351/KJAS.2003.16.2.407

Comparison of GEE Estimators Using Imputation Methods  

김동욱 (성균관대학교 통계학과)
노영화 (성균관대학교 통계학과)
Publication Information
The Korean Journal of Applied Statistics / v.16, no.2, 2003 , pp. 407-426 More about this Journal
Abstract
We consider the missing covariates problem in generalized estimating equations(GEE) model. If the covariate is partially missing, GEE can not be calculated. In this paper, we study the performance of 7 imputation methods to handle missing covariates in GEE models, and the properties of GEE estimators are investigated after missing covariates are imputed for ordinal data of repeated measurements. The 7 imputation methods include i) Naive Deletion ii) Sample Average Imputation iii) Row Average Imputation iv) Cross-wave Regression Imputation v) Carry-over Imputation vi) Bayesian Bootstrap vii) Approximate Bayesian Bootstrap. A Monte-Carlo simulation is used to compare the performance of these methods. For the missing mechanism generating the missing data, we assume ignorable nonresponse. Furthermore, we generate missing covariates with or without considering wave nonresp onse patterns.
Keywords
Missing value; longitudinal data; imputation method; ignorable nonresponse; nonresponse pattern; generalized estimating equations;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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