Fragmentary data is becoming more and more popular in many areas which brings big chal-lenges to researchers and data analysts.Most existing methods dealing with fragmentary data consider a continuous response while i...Fragmentary data is becoming more and more popular in many areas which brings big chal-lenges 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 completed cases and its asymptotic optimality is established.Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented.展开更多
基金The research of Fang was supported by National Key R&D Program of China[grant numbers 2021YFA1000100,2021YFA1000101]National Natural Science Foundation of China[grant numbers 11831008,12071143].
文摘Fragmentary data is becoming more and more popular in many areas which brings big chal-lenges 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 completed cases and its asymptotic optimality is established.Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented.
