This paper provides further contributions to the theory of linear sufficiency in the general Gauss-Markov model E(y)=Xβ,Var (y)=V.The notion of linear sufficiency introduced by Baksalary and Kala(1981) and Drygas(198...This paper provides further contributions to the theory of linear sufficiency in the general Gauss-Markov model E(y)=Xβ,Var (y)=V.The notion of linear sufficiency introduced by Baksalary and Kala(1981) and Drygas(1983) is extended for any specific estimable function c′β.Some general results with respect to the extended concept are obtained.An essential result concerning the former notion is a direct consequence of this paper.展开更多
Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that th</span><span style="font-family:Verdana;">is...Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that th</span><span style="font-family:Verdana;">is technique resides in the structure of an inner product space. Th</span><span style="font-family:Verdana;">e technique uses conditioning </span></span><span style="font-family:Verdana;">of</span><span style="font-family:Verdana;"> an unbiased estimator </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.展开更多
This paper studies a maximum likelihood estimator(MLE) of the parameter for a continuous one-parameter exponential family under ranked set sampling(RSS). The authors first find the optimal RSS according to the charact...This paper studies a maximum likelihood estimator(MLE) of the parameter for a continuous one-parameter exponential family under ranked set sampling(RSS). The authors first find the optimal RSS according to the character of the family, viz, arrange the RSS based on quasi complete and sufficient statistic of independent and identically distributed(iid) samples. Then under this RSS, some sufficient conditions for the existence and uniqueness of the MLE, which are easily used in practice,are obtained. Using these conditions, the existence and uniqueness of the MLEs of the parameters for some usual distributions in this family are proved. Numerical simulations for these distributions fully support the result from the above two step optimizations of the sampling and the estimation method.展开更多
基金the Natural Science Foundation of Guangdong Province(0 1 0 4 86 )
文摘This paper provides further contributions to the theory of linear sufficiency in the general Gauss-Markov model E(y)=Xβ,Var (y)=V.The notion of linear sufficiency introduced by Baksalary and Kala(1981) and Drygas(1983) is extended for any specific estimable function c′β.Some general results with respect to the extended concept are obtained.An essential result concerning the former notion is a direct consequence of this paper.
文摘Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that th</span><span style="font-family:Verdana;">is technique resides in the structure of an inner product space. Th</span><span style="font-family:Verdana;">e technique uses conditioning </span></span><span style="font-family:Verdana;">of</span><span style="font-family:Verdana;"> an unbiased estimator </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.
基金supported by the National Science Foundation of China under Grant Nos.11571133 and11461027the Fundamental Research Funds for the Central Universities under Grant No.20205001515
文摘This paper studies a maximum likelihood estimator(MLE) of the parameter for a continuous one-parameter exponential family under ranked set sampling(RSS). The authors first find the optimal RSS according to the character of the family, viz, arrange the RSS based on quasi complete and sufficient statistic of independent and identically distributed(iid) samples. Then under this RSS, some sufficient conditions for the existence and uniqueness of the MLE, which are easily used in practice,are obtained. Using these conditions, the existence and uniqueness of the MLEs of the parameters for some usual distributions in this family are proved. Numerical simulations for these distributions fully support the result from the above two step optimizations of the sampling and the estimation method.
