Effective transformation-based variable selection under two-fold subarea models in small area estimation

Cai Song (), Rao J. N. K. (), Dumitrescu Laura () and Chatrchi Golshid ()
Additional contact information
Cai Song: Carleton University, Ottawa, ON, Canada .
Rao J. N. K.: Carleton University, Ottawa, ON, Canada .
Dumitrescu Laura: Victoria University of Wellington, Wellington, New Zealand .
Chatrchi Golshid: Statistics Canada, Ottawa, Ontario, ; Canada .

Statistics in Transition New Series, 2020, vol. 21, issue 4, 68-83

Abstract: We present a simple yet effective variable selection method for the two-fold nested subarea model, which generalizes the widely-used Fay-Herriot area model. The twofold subarea model consists of a sampling model and a linking model, which has a nested-error model structure but with unobserved responses. To select variables under the two-fold subarea model, we first transform the linking model into a model with the structure of a regular regression model and unobserved responses. We then estimate an information criterion based on the transformed linking model and use the estimated information criterion for variable selection. The proposed method is motivated by the variable selection method of Lahiri and Suntornchost (2015) for the Fay-Herriot model and the variable selection method of Li and Lahiri (2019) for the unit-level nested-error regression model. Simulation results show that the proposed variable selection method performs significantly better than some naive competitors, especially when the variance of the area-level random effect in the linking model is large.

Keywords: bias correction; conditional AIC; Fay-Herriot model; information criterion. (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.21307/stattrans-2020-031 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:21:y:2020:i:4:p:68-83:n:4

DOI: 10.21307/stattrans-2020-031

Access Statistics for this article

Statistics in Transition New Series is currently edited by Włodzimierz Okrasa

More articles in Statistics in Transition New Series from Statistics Poland
Bibliographic data for series maintained by Peter Golla ().