EM算法是近年来常用的求后验众数的估计的一种数据增广算法,但由于求出其E步中积分的显示表达式有时很困难,甚至不可能,限制了其应用的广泛性.而Monte Carlo EM算法很好地解决了这个问题,将EM算法中E步的积分用Monte Carlo模拟来有效实现,使其适用性大大增强.但无论是EM算法,还是Monte Carlo EM算法,其收敛速度都是线性的,被缺损信息的倒数所控制,当缺损数据的比例很高时,收敛速度就非常缓慢.而Newton-Raphson算法在后验众数的附近具有二次收敛速率.本文提出Monte Carlo EM加速算法,将Monte Carlo EM算法与Newton-Raphson算法结合,既使得EM算法中的E步用Monte Carlo模拟得以实现,又证明了该算法在后验众数附近具有二次收敛速度.从而使其保留了Monte Carlo EM算法的优点,并改进了Monte Carlo EM算法的收敛速度.本文通过数值例子,将Monte Carlo EM加速算法的结果与EM算法、Monte Carlo EM算法的结果进行比较,进一步说明了Monte Carlo EM加速算法的优良性.
Most of the previous researches about portfolio analysis focus on short-selling. In fact, no short-selling is also important because short-selling is not allowed in stock markets of some countries. This paper gives the sufficient and necessary conditions and proposes an optimal algorithm for Markowitz’s mean-variance models and Sharpe’s ratio with no short-selling. The optimal algorithm makes it easier to obtain the efficient frontiers with no short-selling.
