Why Well Spread Probability Samples Are Balanced  

作　　者：Anton Grafstrom Niklas L.P.Lundstrom 

机构地区：[1]Department of Forest Resource Management,Swedish University of Agricultural Sciences,Umea,Sweden [2]Department of Mathematics and Mathematical Statistics,Umea University,Umea,Sweden

出　　处：《Open Journal of Statistics》2013年第1期36-41,共6页统计学期刊（英文）

摘　　要：When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.

关 键 词：Balanced Sample Local Pivotal Method Spatial Balance Spatially Correlated Poisson Sampling Voronoi Polytopes 

分 类 号：R73[医药卫生—肿瘤]