Estimation of an oblique structure via penalized likelihood factor analysis

Kei Hirose, Michio Yamamoto

研究成果: ジャーナルへの寄稿学術誌査読

27 被引用数 (Scopus)


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.

ジャーナルComputational Statistics and Data Analysis
出版ステータス出版済み - 11月 2014

!!!All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 計算数学
  • 計算理論と計算数学
  • 応用数学


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