Normal/Independent Distributions and Their Applications in Robust Regression
Normal/Independent Distributions and Their Applications in Robust Regression
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DOI：
10.1080/10618600.1993.10474606
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发表时间：
1993-06
影响因子：
2.4
通讯作者：
K. Lange;J. Sinsheimer
中科院分区：
文献类型：
--
作者：
K. Lange;J. Sinsheimer
摘要非正态误差分布的极大似然估计提供了一种稳健回归的方法。某些正态/独立分布族对于适应性、稳健回归特别有吸引力。本文回顾了正态/独立分布的性质，并给出了几个新的结果。这些分布的一个主要优点是它们适合EM算法进行最大似然估计。讨论了基于t、斜率和污染正态族的最小Lp回归和自适应稳健回归的EM算法。四个具体的例子说明了不同方法在实际数据上的性能。
Abstract Maximum likelihood estimation with nonnormal error distributions provides one method of robust regression. Certain families of normal/independent distributions are particularly attractive for adaptive, robust regression. This article reviews the properties of normal/independent distributions and presents several new results. A major virtue of these distributions is that they lend themselves to EM algorithms for maximum likelihood estimation. EM algorithms are discussed for least Lp regression and for adaptive, robust regression based on the t, slash, and contaminated normal families. Four concrete examples illustrate the performance of the different methods on real data.