Review Articles

Model averaging for generalized linear models in fragmentary data prediction

Chaoxia Yuan ,

KLATASDS – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China

Yang Wu ,

KLATASDS – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China

Fang Fang

KLATASDS – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China

Pages | Received 01 Feb. 2022, Accepted 18 Jul. 2022, Published online: 30 Jul. 2022,
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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 completed cases and its asymptotic optimality is established. Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented.

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To cite this article: Chaoxia Yuan, Yang Wu & Fang Fang (2022) Model averaging for generalized linear models in fragmentary data prediction, Statistical Theory and Related Fields, 6:4, 344-352, DOI: 10.1080/24754269.2022.2105486 To link to this article: