Optimal model averaging forecasting in high-dimensional survival analysis

Xiaodong Yan, Hongni Wang, Wei Wang, Jinhan Xie, Yanyan Ren and Xinjun Wang

International Journal of Forecasting, 2021, vol. 37, issue 3, 1147-1155

Abstract: This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.

Keywords: Health forecasting; Simulation; Feature screening; Model averaging; Survival analysis; Right-censored data; Ultra-high dimensional data (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:3:p:1147-1155

DOI: 10.1016/j.ijforecast.2020.12.004

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