摘要
Ranked-set sampling(RSS) often provides more efficient inference than simple random sampling(SRS).In this article,we propose a systematic nonparametric technique,RSS-EL,for hypoth-esis testing and interval estimation with balanced RSS data using empirical likelihood(EL).We detail the approach for interval estimation and hypothesis testing in one-sample and two-sample problems and general estimating equations.In all three cases,RSS is shown to provide more efficient inference than SRS of the same size.Moreover,the RSS-EL method does not require any easily violated assumptions needed by existing rank-based nonparametric methods for RSS data,such as perfect ranking,identical ranking scheme in two groups,and location shift between two population distributions.The merit of the RSS-EL method is also demonstrated through simulation studies.
Ranked-set sampling (RSS) often provides more efficient inference than simple random sampling (SRS). In this article, we propose a systematic nonparametric technique, RSS-EL, for hypothesis testing and interval estimation with balanced RSS data using empirical likelihood (EL). We detail the approach for interval estimation and hypothesis testing in one-sample and two-sample problems and general estimating equations. In all three cases, RSS is shown to provide more efficient inference than SRS of the same size. Moreover, the RSS-EL method does not require any easily violated assumptions needed by existing rank-based nonparametric methods for RSS data, such as perfect ranking, identical ranking scheme in two groups, and location shift between two population distributions. The merit of the RSS-EL method is also demonstrated through simulation studies.
基金
supported by National Natural Science Foundation of China (Grant No. 10871037)
