摘要
提出了非线性不等式约束下线性模型回归系数渐进极大似然估计的EM算法,利用极大似然估计的渐近正态性质,将EM算法的M-步转化为随机优化问题,给出了该随机优化问题的极限问题,即利用更易求解的极限问题的最优解来代替原优化问题的最优解,并证明了原优化问题的最优解是依概率收敛于极限问题的最优解.
This paper proposes the EM algorithm for asymptotic ML Estimation under nonlinear inequalities restrictions on the parameter for linear model. By using the asymptotic normality of the maximum likelihood estimators, we change this kind of estimation problem to a stochastic optimization problem in the M-step, and give the limit problem of the stochastic optimization problem, that is, we get the optimal solution of the stochastic optimization problem by using the limit problem whose optimal solution is easily computed. It proves that the optimal solution of the stochastic optimization problem is converged to the optimal solution of the limit problem in probability.
出处
《河南大学学报（自然科学版）》
CAS
北大核心
2008年第3期231-234,共4页
Journal of Henan University:Natural Science
作者简介
文生兰（1981-），女，河南唐河人，硕士研究生．E-mail：wenshenglan@sina．com．
