International Journal of Mathematics and Computational Science
Articles Information
International Journal of Mathematics and Computational Science, Vol.4, No.3, Sep. 2018, Pub. Date: Jun. 14, 2018
On Estimation Methods for Binary Logistic Regression Model with Missing Values
Pages: 79-85 Views: 505 Downloads: 500
Authors
[01] Mohamed Reda Abonazel, Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Cairo, Egypt.
[02] Mohamed Gamal Ibrahim, Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Cairo, Egypt.
Abstract
This paper reviews some estimation methods for the binary logistic regression model with missing data in dependent and/or independent variables. Moreover, we present an empirical study for assessing the performance of these estimation methods under the existence of missing data. The results indicated that the regression imputation method is a very appropriate method for estimating the missing values in this model.
Keywords
EM Algorithm, Incomplete Data, Maximum Likelihood Estimation, Regression Imputation
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