Abstract
This chapter discusses asymptotically efficient estimation of nonparametric regression coefficients. Asymptotically efficient is meant in the classical sense of minimal variance of the limiting normal distribution. It presents a method to obtain asymptotically efficient estimators of regression parameters when the density function is unknown. The method involves a nontrivial use of the results of a previous study. Without the requirement of symmetry of the density function or an equivalent requirement, the parameter θ1 is not uniquely determined and, hence, cannot be estimated. The parameter θ2 can be estimated without this requirement. The chapter discusses different assumption and notation. It also describes the reduction of the original problem to an asymptotically equivalent problem.
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 callDisclaimer: 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.
