  Fragmentary data is becoming more and more popular in many areas which brings
big challenges to researchers and data analysts. Most existing methods dealing
with fragmentary data consider a continuous response while in many applications
the response variable is discrete. In this paper we propose a model averaging
method for generalized linear models in fragmentary data prediction. The
candidate models are fitted based on different combinations of covariate
availability and sample size. The optimal weight is selected by minimizing the
Kullback-Leibler loss in the com?pleted cases and its asymptotic optimality is
established. Empirical evidences from a simulation study and a real data
analysis about Alzheimer disease are presented.
