The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are a...The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.展开更多
This paper discusses the strong consistency of M estimator of regression parameter in linear model for negatively associated samples. As a result, the author extends Theorem 1 and Theorem 2 of Shanchao YANG （2002） t...This paper discusses the strong consistency of M estimator of regression parameter in linear model for negatively associated samples. As a result, the author extends Theorem 1 and Theorem 2 of Shanchao YANG （2002） to the NA errors without necessarily imposing any extra condition.展开更多
The construction of confidence intervals for quantiles of a population under a associated sample is studied by using the blockwise technique. It is shown that the blockwise empirical likelihood （EL） ratio statistic ...The construction of confidence intervals for quantiles of a population under a associated sample is studied by using the blockwise technique. It is shown that the blockwise empirical likelihood （EL） ratio statistic is asymptotically X2-type distributed, which is used to obtain EL-based confidence intervals for quantiles of a population.展开更多
In this paper, the authors obtain the joint empirical likelihood confidence regions for a finite number of quantiles under negatively associated samples. As an application of this result, the empirical likelihood conf...In this paper, the authors obtain the joint empirical likelihood confidence regions for a finite number of quantiles under negatively associated samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also developed.展开更多
基金supported by the National Natural Science Foundation of China(11271088,11361011,11201088)the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
文摘The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.
基金The research is supported by National Natural Science Foundation of China(No.10661006)the Support Program for 100 Young and Middle-aged Disciplinary Leaders in Guangxi Higher Education Institutions([2005]64),and Guangxi Science Foundation(0447096)
文摘This paper discusses the strong consistency of M estimator of regression parameter in linear model for negatively associated samples. As a result, the author extends Theorem 1 and Theorem 2 of Shanchao YANG （2002） to the NA errors without necessarily imposing any extra condition.
基金Supported by the National Natural Science Foundation of China(No.11271088,11201088,11361011)the Natural Science Foundation of Guangxi(N0.2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)+1 种基金the New Century Ten,Hundred and Thousand Talents Project of Guangxithe Youth Foundation of Guangxi Normal University
文摘The construction of confidence intervals for quantiles of a population under a associated sample is studied by using the blockwise technique. It is shown that the blockwise empirical likelihood （EL） ratio statistic is asymptotically X2-type distributed, which is used to obtain EL-based confidence intervals for quantiles of a population.
基金supported by the National Natural Science Foundation of China under Grant Nos.1127108811361011+3 种基金11201088the Natural Science Foundation of Guangxi under Grant No.2013GXNSFAA0190042013 GXNSFAA 0190072013GXNSFBA019001
文摘In this paper, the authors obtain the joint empirical likelihood confidence regions for a finite number of quantiles under negatively associated samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also developed.
