|
|
|
|
| LEADER |
01272 am a22001333u 4500 |
| 001 |
350426 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Escobar, E.L.
|e author
|
| 700 |
1 |
0 |
|a Berger, Y.G.
|e author
|
| 245 |
0 |
0 |
|a A jackknife variance estimator for self-weighted two-stage samples
|
| 260 |
|
|
|c 2013-04.
|
| 856 |
|
|
|z Get fulltext
|u https://eprints.soton.ac.uk/350426/1/Escobar_Berger_2013_Sinica.pdf
|
| 520 |
|
|
|a Self-weighted two-stage sampling designs are popular in practice as they simplify field-work. It is common in practice to compute variance estimates only from the first sampling stage, neglecting the second stage. This omission may induce a bias in variance estimation; especially in situations where there is low variability between clusters or when sampling fractions are non-negligible. We propose a design-consistent jackknife variance estimator that takes account of all stages via deletion of clusters and observations within clusters. The proposed jackknife can be used for a wide class of point estimators. It does not need joint-inclusion probabilities and naturally includes finite population corrections. A simulation study shows that the proposed estimator can be more accurate than standard jackknifes (Rao, Wu, and Yue (1992)) for self-weighted two-stage sampling designs.
|
| 655 |
7 |
|
|a Article
|
