supported by the National Natural Science Foundation of China(11271088,11361011,11201088);the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
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...
Supported by the National Natural Science Foundation of China(No.11271088,11361011,11201088);Natural Science Foundation of Guangxi(No.2013GXNSFAA(019004 and 019007),2013GXNSFBA019001)
Oonsider two linear models Xi = U'β + ei, Yj = V1/2y + ηj with response variables missing at random. In this paper, we assume that X, Y are missing at random (MAR) and use the inverse probability weighted imput...
Supported by the National Natural Science Foundation of China(11271088,11361011,11201088);the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals ...
Supported by the National Natural Science Foundation of China(No.11271088,11201088,11361011);the Natural Science Foundation of Guangxi(N0.2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001);the New Century Ten,Hundred and Thousand Talents Project of Guangxi;the Youth Foundation of Guangxi Normal University
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 National Natural Science Foundation of China(11271088,11201088);the Guangxi"Bagui Scholar"Special Project Foundation;the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
Supported by the National Natural Science Foundation of China(No.11271088,11361011,11201088);Guangxi"Bagui Scholar"Special Project Foundation;the Natural Science Foundation of Guangxi(No.2013GXNS-FAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
Suppose that we have a partially linear model Yi = xiβ + g(ti) +εi with independent zero mean errors εi, where (xi,ti, i = 1, ... ,n} are non-random and observed completely and (Yi, i = 1,...,n} are missing a...