Kernel-based methods for combining information of several frame surveys Sánchez Borrego, Ismael Ramón Rueda García, María Del Mar Arcos Cebrián, Antonio Kernel regression nonparametric regression dual frame survey multiple frame survey model-assisted estimation 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. 2021-05-14T10:41:03Z 2021-05-14T10:41:03Z 2018-10-03 journal article Sánchez-Borrego, I., Arcos, A. & Rueda, M. Kernel-based methods for combining information of several frame surveys. Metrika 82, 71–86 (2019). https://doi.org/10.1007/s00184-018-0686-8 http://hdl.handle.net/10481/68525 https://doi.org/10.1007/s00184-018-0686-8 eng MTM2015-63609-R SEJ2954 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ open access Atribución-NoComercial-SinDerivadas 3.0 España