An efficient design for model discrimination and parameter estimation in linear models

• 成果类型: Article
• 署名作者: Biswas, A; Chaudhuri, P
• 署名单位: Indian Statistical Institute; Indian Statistical Institute Kolkata; Indian Statistical Institute; Indian Statistical Institute Kolkata
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/89.3.709
• 发表日期: 2002
• 页码: 709718
• 关键词: 2 rival models; POLYNOMIAL REGRESSION; nonlinear experiments; planning experiments; mechanistic models; criteria

摘要

We consider experimental designs in a regression set-up where the unknown regression function belongs to a known family of nested linear models. The objective of our design is to select the correct model from the family of nested models as well as to estimate efficiently the parameters associated with that model. We show that our proposed design is able to choose the true model with probability tending to one as the number of trials grows to infinity. We also establish that our selected design converges to the optimal design distribution for the true linear model ensuring asymptotic efficiency of least squares estimators of model parameters.