Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators
| Résumé |
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. |
| Mots-clés |
: 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 | Article de périodique (Anglais) |
| Date de publication | 27-6-2014 |
| Nom du périodique | Survey Methodology |
| Volume | 40 |
| Numéro | 1 |
| Pages | 61-79 |
| URL | http://www.statcan.gc.ca/pub/12-001-x/12-001-x2014001-eng... |
| Liée au projet | Convention Université de Neuchâtel/Office fédéral de la s... |