Generalized Linear Model for Binary Data with Missing Values: An EM Algorithm Approach
نویسنده
چکیده
Abstract: A procedure is derived for estimating the parameter in case of missing data. The missing data mechanism is considered as missing at random (MAR) and non-ignorable. Here we use EM algorithm for logit link approach in generalized linear model. The logit link approach shows that it can effectively estimate the value of a categorical variable when we have information on the other categorical variables. In this method the variable with missing values is considered as dependent variable. In addition a real data set for low birth weight is presented to illustrate the method proposed.
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