Best Invariant and Minimax Estimation of Quantiles in Finite Populations.
J Stat Plan Inference
; 141(8): 2633-2644, 2011 Aug 01.
Article
en En
| MEDLINE
| ID: mdl-21643478
ABSTRACT
The theoretical literature on quantile and distribution function estimation in infinite populations is very rich, and invariance plays an important role in these studies. This is not the case for the commonly occurring problem of estimation of quantiles in finite populations. The latter is more complicated and interesting because an optimal strategy consists not only of an estimator, but also of a sampling design, and the estimator may depend on the design and on the labels of sampled individuals, whereas in iid sampling, design issues and labels do not exist.We study estimation of finite population quantiles, with emphasis on estimators that are invariant under the group of monotone transformations of the data, and suitable invariant loss functions. Invariance under the finite group of permutation of the sample is also considered. We discuss nonrandomized and randomized estimators, best invariant and minimax estimators, and sampling strategies relative to different classes. Invariant loss functions and estimators in finite population sampling have a nonparametric flavor, and various natural combinatorial questions and tools arise as a result.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
Idioma:
En
Revista:
J Stat Plan Inference
Año:
2011
Tipo del documento:
Article
País de afiliación:
Estados Unidos