The Use of Data-driven Transformations and Their Applicability in Small Area Estimation

Natalia Rojas-Perilla

WISTA – Wirtschaft und Statistik, 2021, vol. 73, issue 1, 59-66

Abstract: In general, researchers have been using data transformations as a go-to tool to assist scientific work under the classical and linear mixed regression models instead of developing new theories, applying complex methods or extending software functions. However, transformations are often automatically and routinely applied without considering different aspects on their utility. This work summarizes the main findings from the paper by the author (Rojas-Perilla, 2018), which presents a unified theory of datadriven transformations for linear and linear mixed regression models that includes applications to small area prediction and the development of open source software.

Keywords: Data-driven transformations; small area estimation; poverty mapping; generalized regression models; datengetriebene Transformationen; Small-Area-Schätzung; Armutsabbildung; verallgemeinerte Regressionsmodelle (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:wistat:230955

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