Abstract

In this paper, we study the adaptive kernel estimator for the bidimensional extension of Foster, Greer and Thorbecke class of measures. The asymptotic normality of the estimator is established. Next, we show how the proposed estimator can generate sequential confidence intervals by a moving adaptive kernel process. As an illustration, we determine the confidence intervals for different regions of Senegal. The study of this application demonstrated that our methodology is not only more efficient than the classical and empirical estimator, but it also provides better confidence intervals for the poverty index

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.