 B-Spline Estimation in a Survey Sampling FrameworkCamelia Goga ()
A chapter in Advances in Contemporary Statistics and Econometrics, 2021, pp 79-99 from Springer Abstract: Abstract Nonparametric regression models have been used more and more over the last years to model survey data and incorporate efficiently auxiliary information in order to improve the estimation of totals, means or other study parameters such as Gini index or poverty rate. B-spline nonparametric regression has the benefit of being very flexible in modeling nonlinear survey data while keeping many similarities and properties of the classical linear regression. This method proved to be efficient for deriving a unique system of weights which allowed to estimate in an efficient way and simultaneously many study parameters. Applications on real and simulated survey data showed its high efficiency. This paper aims at giving a review of applications of the B-spline nonparametric regression in a survey sampling framework and design-based approach. Handling item nonresponse by B-spline modeling is also considered. This review includes also new properties and improved consistency rates of the suggested penalized and unpenalized B-spline estimators. Date: 2021 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-73249-3_5 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-73249-3_5 Access Statistics for this chapter More chapters in Springer Books from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.