A sequential design for estimating a nonlinear parametric function

A fully-sequential design for estimating a nonlinear function of the parameters in the simple linear regression model is proposed and its asymptotic behavior is investigated both theoretically and by simulation. The design requires that the observations be taken at x=±1 and specifies whether the nex...

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Veröffentlicht in:	Applied mathematics and computation Jg. 138; H. 1; S. 113 - 120
Hauptverfasser:	Rekab, Kamel, Tahir, Mohamed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY Elsevier Inc 01.06.2003 Elsevier
Schlagworte:	Asymptotically optimal fixed design Exact sciences and technology Experimental design Fisher information matrix Fully-sequential design Least squares estimator Linear inference, regression Mathematics Probability and statistics Sciences and techniques of general use Second order approximation Simple linear regression model Statistics Least squares estimator Second order approximation Simple linear regression model Fully-sequential design Asymptotically optimal fixed design Fisher information matrix Parameter estimation Sequential design Linear regression Asymptotic behavior Non linear function Asymptotic optimality Probability distribution Linear model Fisher information Simulation Regression model Least squares method Experimental design Asymptotic approximation Optimal design(statistics)
ISSN:	0096-3003, 1873-5649
Online-Zugang:	Volltext
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