Variance Estimation for Regression Imputed Quantiles, A first Step towards Variance Estimation for Inequality Indicators
Résumé |
In a sample survey only a sub-part of the selected sample has
answered (total non-response, treated by re-weighting). Moreover,
some respondents did not answer all questions (partial
non-response, treated through imputation). One is interested in
income type variables. One further supposes here that the
imputation is carried out by a regression. The idea presented by
Deville and Särndal in 1994 is resumed, which consists in
constructing an unbiased estimator of the variance of a total based
solely on the known information (on the selected sample and the
subset of respondents). While these authors dealt with a
conventional total of an interest variable y, a similar development
is reproduced in the case where the considered total is one of the
linearized variable of quantiles or of inequality indicators, and
that, furthermore, it is computed from the imputed variable y. By
means of simulations on real survey data, one shows that regression
imputation can have an important impact on the bias and variance
estimations of inequality indicators. This leads to a method
capable of taking into account the variance due to imputation in
addition to the one due to the sampling design in the cases of
quantiles. |
Mots-clés |
Influence function, SILC survey, linearization, bias, simulations, Laeken indicators |
Citation | Graf, E. (2014). Variance Estimation for Regression Imputed Quantiles, A first Step towards Variance Estimation for Inequality Indicators. Presented at COMPSTAT, Geneva. |
Type | Présentation (Anglais) |
Date | 20-8-2014 |
Evénement | COMPSTAT (Geneva) |
URL | http://compstat2014.org/ |
Liée au projet | Convention Université de Neuchâtel/Office fédéral de la s... |