Abstract

This paper is concerned with the problem of optimal designs for both linear and nonlinear regression models using the second-order least squares estimator when the error distribution is asymmetric. A new class of R-optimality criterion is proposed based on the second-order least squares estimator. An equivalence theorem for R-optimality is then established and used to check the optimality of designs. Moreover, several invariance properties of R-optimal designs are investigated. A few examples are presented for illustration and the relative efficiency comparisons between the second-order least squares estimator and the ordinary least squares estimator are discussed via the new criterion.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.