Linearization versus bootstrap for variance estimation of the change between Gini indexes
| Affiliation auteurs | Affiliation ok |
| Titre | Linearization versus bootstrap for variance estimation of the change between Gini indexes |
| Type de publication | Journal Article |
| Year of Publication | 2018 |
| Auteurs | Chauvet G, Goga C |
| Journal | SURVEY METHODOLOGY |
| Volume | 44 |
| Pagination | 17-42 |
| Date Published | JUN |
| Type of Article | Article |
| ISSN | 0714-0045 |
| Mots-clés | Composite estimator, Horvitz-Thompson estimator, Influence function, Intersection estimator, Replication weights, Two-dimensional sampling design, Two-sample survey, Union estimator, Variance estimation |
| Résumé | This paper investigates the linearization and bootstrap variance estimation for the Gini coefficient and the change between Gini indexes at two periods of time. For the one-sample case, we use the influence function linearization approach suggested by Deville (1999), the without-replacement bootstrap suggested by Gross (1980) for simple random sampling without replacement and the with-replacement of primary sampling units described in Rao and Wu (1988) for multistage sampling. To obtain a two-sample variance estimator, we use the linearization technique by means of partial influence functions (Goga, Deville and Ruiz-Gazen, 2009). We also develop an extension of the studied bootstrap procedures for two-dimensional sampling. The two approaches are compared on simulated data. |