摘要
最小二乘估计容易受奇异点的影响,最小一乘估计是稳健估计,可以很好地克服这个缺陷,但计算困难.基于非退化模型假设下的稳定极点理论,本文找到了快速准确求解最小一乘估计的迭代算法,并给出算法的计算过程及与线性规划求解的比较,较好地解决了最小一乘估计计算难的问题,使其成为有效的参数估计方法.
Least square estimator(LSE) is disturbed easily by singular point;least absolute deviation estimator(LADE) can overcome the influence of singular point,but it is difficult in calculation.A convergent algorithm for LADE based on the stable pole theorem of LADE under non-degenerate model is obtained in this paper.The progress of algorithm and comparison of linear programming are derived.Further this algorithm makes LADE more effective.
出处
《应用概率统计》
CSCD
北大核心
2008年第6期621-630,共10页
Chinese Journal of Applied Probability and Statistics
基金
福州大学科技发展基金资助(2005-XQ-17)
关键词
最小一乘估计
最小二乘估计
迭代算法
Least absolute deviation,least square,convergent algorithm.
