Frequentist model averaging estimation for the censored partial linear quantile regression model

•We develop a model averaging procedure for a partial linear regression model.•The procedure depicts the comprehensive character of the distribution of the response.•A resampling method is developed to estimate the covariance matrix of the estimators.

In this article, we propose a focused information criterion (FIC) and develop a frequentist model averaging estimation procedure for a partial linear regression model when the response is randomly right-censored. The proposed procedure is based on the quantile regression and can depict the comprehensive character of the distribution of the response by means of modeling different quantiles. The large sample properties of the proposed estimators are established, and their finite sample properties are examined through simulation studies. An application to a primary biliary cirrhosis data set is provided.