Constrained design strategies for improving normal approximations in nonlinear regression problems

DOI10.1016/S0378-3758(01)00239-7zbMath0988.62043MaRDI QIDQ1600751

Kathryn Chaloner, Merlise A. Clyde

Publication date: 16 June 2002

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

zbMATH Keywords

curvature measures

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15) General nonlinear regression (62J02)

Related Items (4)

Design issues for generalized linear models: a review ⋮ Optimum design accounting for the global nonlinear behavior of the model ⋮ Non-linear design problem in a chemical kinetic model with non-constant error variance ⋮ Designs in nonlinear regression by stochastic minimization of functionals of the mean square error matrix

Uses Software

• LISP-STAT
• NPSOL

Cites Work

• Statistical decision theory and Bayesian analysis. 2nd ed
• Optimal Bayesian design applied to logistic regression experiments
• On linear regression designs which maximize information
• Nonlinear experimental design based on the distribution of estimators
• Defining the curvature of a statistical problem (with applications to second order efficiency)
• A note on optimal Bayesian design for nonlinear problems
• Some diagnostics of maximum likelihood and posterior nonnormality
• The geometry of asymptotic inference. With comments and a rejoinder by the author
• Bayesian experimental design: A review
• DESIGN OF EXPERIMENTS IN NON-LINEAR SITUATIONS
• The Equivalence of Two Extremum Problems
• A Quadratic Design Criterion for Precise Estimation in Nonlinear Regression Models
• Marginal Curvatures and Their Usefulness in the Analysis of Nonlinear Regression Models
• Recent Advances in Nonlinear Experimental Design
• The Equivalence of Constrained and Weighted Designs in Multiple Objective Design Problems
• Transformation of the Independent Variables
• Locally Optimal Designs for Estimating Parameters
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