Estimation of an oblique structure via penalized likelihood factor analysis
Estimation of an oblique structure via penalized likelihood factor analysis
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DOI：
10.1016/j.csda.2014.05.011
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发表时间：
2014-11-01
影响因子：
1.8
通讯作者：
Yamamoto, Michio
中科院分区：
文献类型：
--
作者：
Hirose, Kei;Yamamoto, Michio
研究了因子分析模型中基于laso型惩罚似然过程的稀疏估计问题。通常，模型估计假设公共因素是正交的（即不相关）。然而，如果共同因素是相关的，基于正交模型的套索式惩罚方法往往会估计出错误的模型。为了克服这个问题，在模型中加入了因子相关性。与正交模型中的参数一起，通过最大惩罚似然过程估计这些相关性。整个解决方案是由EM算法计算与坐标下降，使各种凸和非凸惩罚的应用。所提出的方法即使在变量数量超过观测值的情况下也适用。通过蒙特卡罗仿真验证了该策略的有效性，并通过实际数据分析验证了其有效性。(C) 2014 Elsevier B.V.版权所有
The problem of sparse estimation via a lasso-type penalized likelihood procedure in a factor analysis model is considered. Typically, model estimation assumes that the common factors are orthogonal (i.e., uncorrelated). However, if the common factors are correlated, the lasso-type penalization method based on the orthogonal model frequently estimates an erroneous model. To overcome this problem, factor correlations are incorporated into the model. Together with parameters in the orthogonal model, these correlations are estimated by a maximum penalized likelihood procedure. Entire solutions are computed by the EM algorithm with a coordinate descent, enabling the application of a wide variety of convex and nonconvex penalties. The proposed method is applicable even when the number of variables exceeds that of observations. The effectiveness of the proposed strategy is evaluated by Monte Carlo simulations, and its utility is demonstrated through real data analysis. (C) 2014 Elsevier B.V. All rights reserved.