Design of experiments with unknown parameters in variance

Autor:	Robert C. Gagnon, Sergei L. Leonov, Valerii V. Fedorov
Rok vydání:	2002
Předmět:	Analysis of covariance Optimal design Estimator Variance (accounting) Management Science and Operations Research M-estimator General Business Management and Accounting Iterated function Modeling and Simulation Calculus Applied mathematics Statistical theory Variance function Mathematics
Zdroj:	Applied Stochastic Models in Business and Industry. 18:207-218
ISSN:	1526-4025 1524-1904
DOI:	10.1002/asmb.474
Popis:	Model fitting when the variance function depends on unknown parameters is a popular problem in many areas of research. Iterated estimators which are asymptotically equivalent to maximum likelihood estimators are proposed and their convergence is discussed. From a computational point of view, these estimators are very close to the iteratively reweighted least-squares methods. The additive structure of the corresponding information matrices allows us to apply convex design theory which leads to optimal design algorithms. We conclude with examples which illustrate how to bridge our general results with specific applied needs. In particular, a model with experimental costs is introduced and is studied within the normalized design paradigm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b8cbd5f04d5c5d921bf5f2392feb3d7e https://doi.org/10.1002/asmb.474 Zobrazit plný text záznamu Plný text ve formátu PDF