TY - JOUR
T1 - Best invariant and minimax estimation of quantiles in finite populations
AU - Malinovsky, Yaakov
AU - Rinott, Yosef
PY - 2011/8
Y1 - 2011/8
N2 - 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 the 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.
AB - 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 the 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.
KW - Behavioral and randomized estimators
KW - Loss functions
KW - Median
KW - Monotone transformations
KW - Sampling and estimation strategies
KW - Simple random sample
UR - http://www.scopus.com/inward/record.url?scp=79954576618&partnerID=8YFLogxK
U2 - 10.1016/j.jspi.2011.02.016
DO - 10.1016/j.jspi.2011.02.016
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AN - SCOPUS:79954576618
SN - 0378-3758
VL - 141
SP - 2633
EP - 2644
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 8
ER -