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Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators
Abstract We have used the generalized linearization technique based on the concept of influence function, as Osier has done (Osier 2009), to estimate the variance of complex statistics such as Laeken indicators. Simulations conducted using the R language show that the use of Gaussian kernel estimation to estimate an income density function results in a strongly biased variance estimate. We are proposing two other density estimation methods that significantly reduce the observed bias. One of the methods has already been outlined by Deville (2000). The results published in this article will help to significantly improve the quality of information on the precision of certain Laeken indicators that are disseminated and compared internationally.
   
Keywords : influence function; EU-SILC survey; non-linear statistics; poverty and inequality indicators
   
Citation Graf, E., & Tillé, Y. (2014). Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators. Survey Methodology, 40(1), 61-79.
   
Type Journal article (English)
Date of appearance 27-6-2014
Journal Survey Methodology
Volume 40
Issue 1
Pages 61-79
URL http://www.statcan.gc.ca/pub/12-001-x/12-001-x2014001-eng...
Related project Convention Université de Neuchâtel/Office fédéral de la s...