Kernel-based methods for combining information of several frame surveys

I. Sánchez-Borrego (), A. Arcos () and M. Rueda ()
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I. Sánchez-Borrego: University of Granada
A. Arcos: University of Granada
M. Rueda: University of Granada

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 1, No 4, 86 pages

Abstract: Abstract A sample selected from a single sampling frame may not represent adequatly the entire population. Multiple frame surveys are becoming increasingly used and popular among statistical agencies and private organizations, in particular in situations where several sampling frames may provide better coverage or can reduce sampling costs for estimating population quantities of interest. Auxiliary information available at the population level is often categorical in nature, so that incorporating categorical and continuous information can improve the efficiency of the method of estimation. Nonparametric regression methods represent a widely used and flexible estimation approach in the survey context. We propose a kernel regression estimator for dual frame surveys that can handle both continuous and categorical data. This methodology is extended to multiple frame surveys. We derive theoretical properties of the proposed methods and numerical experiments indicate that the proposed estimator perform well in practical settings under different scenarios.

Keywords: Kernel regression; Nonparametric regression; Dual frame survey; Multiple frame survey; Model-assisted estimation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00184-018-0686-8

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