In this paper, a semiparametric two-sample density ratio model is considered and the empirical likelihood method is applied to obtain the parameters estimation. A commonly occurring problem in computing is that the em...In this paper, a semiparametric two-sample density ratio model is considered and the empirical likelihood method is applied to obtain the parameters estimation. A commonly occurring problem in computing is that the empirical likelihood function may be a concaveconvex function. Here a simple Lagrange saddle point algorithm is presented for computing the saddle point of the empirical likelihood function when the Lagrange multiplier has no explicit solution. So we can obtain the maximum empirical likelihood estimation （MELE） of parameters. Monte Carlo simulations are presented to illustrate the Lagrange saddle point algorithm.展开更多
基金Supported by the National Natural Science Foundation of China (Grant Nos. 1093100211071035+2 种基金7090101671171035)Excellent Talents Program of Liaoning Educational Committee (Grant No. 2008RC15)
文摘In this paper, a semiparametric two-sample density ratio model is considered and the empirical likelihood method is applied to obtain the parameters estimation. A commonly occurring problem in computing is that the empirical likelihood function may be a concaveconvex function. Here a simple Lagrange saddle point algorithm is presented for computing the saddle point of the empirical likelihood function when the Lagrange multiplier has no explicit solution. So we can obtain the maximum empirical likelihood estimation （MELE） of parameters. Monte Carlo simulations are presented to illustrate the Lagrange saddle point algorithm.
