A STATISTICAL APPROACH TO ADAPTIVE PARAMETER TUNING IN NATURE-INSPIRED OPTIMIZATION AND OPTIMAL SEQUENTIAL DESIGN OF DOSE-FINDING TRIALS

Kwok Pui Choi, Tze Leung Lai, Xin T. Tong, Weng Kee Wong

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

Nature-inspired metaheuristic algorithms have become increasingly popular in the last couple of decades, and now constitute a major toolbox for tackling complex high-dimensional optimization problems. Using group sequential experimentation, adaptive design, multi-armed bandits, and bootstrap resampling methods, this study develops a novel statistical methodology for efficient and systematic group sequential selection of the tuning parameters, which are widely recognized as pivotal to the success of metaheuristic optimization algorithms in practice, as new information accumulates during the course of an experiment. The methodology is applied to compute optimal experimental designs in nonlinear regression models, and is illustrated with solutions of long-standing optimal design problems in early-phase dose-finding oncology trials.

原文English
頁(從 - 到)2381-2401
頁數21
期刊Statistica Sinica
31
DOIs
出版狀態Published - 2021

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 統計、概率和不確定性

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