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Meeting-ID: 280 519 2121
Passwort: 584190

Abstract:

This work introduces an extension of the novel batchwise backfitting algorithm for the simultaneous estimation and model choice for big discrete time-to-event data with additive predictors. The algorithm is designed to efficiently handle large datasets while incorporating a boosting-type approach for automated variable selection. An extensive simulation study is conducted to evaluate the performance of the algorithm against established methods. The effectiveness is further demonstrated by modelling infant mortality in ten Eastern sub-Saharan African countries. The results highlight the algorithm's strong estimation performance, excellent variable selection capabilities and scalability for large datasets.