On information matrices in nonlinear experimental design

DOI10.1016/0378-3758(89)90009-8zbMath0667.62048OpenAlexW2090837293MaRDI QIDQ1117645

Andrej Pázman

Publication date: 1989

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(89)90009-8

zbMATH Keywords

nonlinear least squaresGaussian nonlinear regression modelnonasymptotical approximative probability density of the least squares estimatesnonlinear experimental design

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Point estimation (62F10) General nonlinear regression (62J02)

Related Items (5)

Unnamed Item ⋮ Invited discussion paper small-sample distributional properties of nonlinear regression estimators (a geometric approach) ⋮ Nonlinear experimental design based on the distribution of estimators ⋮ Results on nonlinear least squares estimators under nonlinear equality constraints ⋮ Unnamed Item

Cites Work

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• Accounting for intrinsic nonlinearity in nonlinear regression parameter inference regions
• Saddlepoint approximations for curved exponential families
• On linear regression designs which maximize information
• Application of a Measure of Information to the Design and Comparison of Regression Experiments
• A Quadratic Design Criterion for Precise Estimation in Nonlinear Regression Models
• On Formulas for the Distribution of Nonlinear L.S. Estimates
• Asymptotic Properties of Non-Linear Least Squares Estimators

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