ICAOD: An R Package for Finding Optimal designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm

Ehsan Masoudi, Heinz Holling, Weng Kee Wong, Seongho Kim

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Optimal design ideas are increasingly used in different disciplines to rein in experimental costs. Given a nonlinear statistical model and a design criterion, optimal designs determine the number of experimental points to observe the responses, the design points and the number of replications at each design point. Currently, there are very few free and effective computing tools for finding different types of optimal designs for a general nonlinear model, especially when the criterion is not differentiable. We introduce an R package ICAOD to find various types of optimal designs and they include locally, minimax and Bayesian optimal designs for different nonlinear statistical models. Our main computational tool is a novel metaheuristic algorithm called imperialist competitive algorithm (ICA) and inspired by socio-political behavior of humans and colonialism. We demonstrate its capability and effectiveness using several applications.

原文English
頁(從 - 到)20-45
頁數26
期刊R Journal
14
發行號3
DOIs
出版狀態Published - 2022

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 數值分析
  • 統計、概率和不確定性

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