大阪大学 大学院人間科学研究科 行動統計科学研究分野

Members & Publications

Members

Prof.

Kohei Adachi (President of the Japanese Society of Computational Statistics, Elected Member in the International Statistical Institute) Click Here for Activities

Accoc. Prof.

Michio Yamamoto
Web site

D2

Hiroe Seto
Web site

M2

嶋田直也, 曽我部泰誠, 宮林剛士, 入江一月 (岡山大学), 塩飽大基 (岡山大学)

M1

一方井優佑, 喬国瑋, 橋本捷矢, 坪田有司, 三浦ゆき乃

B4

大杉勇心

B3

岡響希, 岡本颯平

Major Publications (since 2009)

  • Adachi, K. (2016). Matrix-based introduction to multivariate data analysis. Springer.
  • Mori, Y., Kuroda, M., & Makino, N. (2016). Nonlinear principal component analysis and its applications. Springer.
  • Ikemoto, H. & Adachi, K. (2016). Sparse Tucker2 analysis of three-way data subject to a constrained number of zero elements in a core array. Computational Statistics and Data Analysis, 98, 1-18.
  • Adachi, K. & Trendafilov, N. T. (2016). Sparse principal component analysis subject to prespecified cardinality of loadings.Computational Statistics, 31, 1403-1427.
  • Uno, K., Satomura, H., & Adachi, K. (2016). Fixed factor analysis with clustered factor score constraint. Computational Statistics and Data Analysis, 94, 265–274.
  • Adachi, K. (2016). Three-way principal component analysis with its applications to psychology. In T. Sakata (Ed.), Applied matrix and tensor variate data analysis, pp. 1-21. Springer.
  • Trendafilov, N. T. & Adachi, K. (2015). Sparse versus simple structure loadings. Psychometrika, 80, 776-790.
  • Adachi, K. (2015). A new algorithm for generalized least squares factor analysis with a majorization technique. Open Journal of Statistics, 5, 165-172.
  • Makino, N. (2015). Generalized data-fitting factor analysis with multiple quantification of categorical variables. Computational Statistics, 30, 279-292.
  • Adachi, K. & Trendafilov, N. T. (2015). Sparse orthogonal factor analysis. In M. Carpita, E. Brentari., & E. M. Qannari (Eds.), Advances in latent variables, pp. 227-239. Springer.
  • Takahashi, R. & Makino, M. (2015). Pairwise constrained k-means via permutation matrix. Bulletin of the Computational Statistics of Japan, 28, 105-119 (in Japanese).
  • Adachi, K. (2015). Matrix-intensive approach to factor analysis. Japanese Journal of Statistics, 44, 363-382 (in Japanese).
  • Nakamura, Y. (2015). Nonmetric three-mode principal component analysis for qualitative data. Japanese Journal of Behaviormetrics, 42, 105-115 (in Japanese).
  • Satomura, H & Adachi, K. (2013). Oblique rotation in canonical correlation analysis reformulated as maximizing the generalized coefficient of determination. Psychometrika, 78, 526-537.
  • Adachi, K. (2013). Generalized joint Procrustes analysis. Computational Statistics, 28, 2449-2464.
  • Adachi, K. (2013). Factor analysis with EM algorithm never gives improper solutions when sample covariance and initial parameter matrices are proper. Psychometrika, 78, 380-394.
  • Adachi, K. (2013). A restrained condition number least squares technique with its applications to avoiding rank deficiency. Journal of the Japanese Society of Computational Statistics, 26, 39-51.
  • Hashimoto, S. (2013). Bayesian factor analysis for obtaining simplimax solutions with an unspecified number of zeros. Behaviormetrika, 40, 69-83.
  • Adachi, K. (2012). Some contributions to data-fitting factor analysis with empirical comprisons to covariance-fitting factor analysis. Journal of the Japanese Society of Computational Statistics, 25, 25-38.
  • Adachi, K. (2011). Three-way Tucker2 component analysis solutions of stimuli × responses × individuals data with simple structure and the fewest core differences. Psychometrika, 76, 285-305.
  • Adachi, K. (2011). Constrained principal component analysis of standardized data for biplots with unit-length variable vectors. Advances in Data Analysis and Classification，5, 23-36.
  • Adachi, K. & Murakami, T. (2011). Nonmetric multivariate analysis: From principal component analysis to multiple correspondence analysis. Asakura-Shoten (in Japanese).
  • Adachi, K. (2011). Fixed size clustering with least squares permutation. Bulletin of Data Analysis of Japanese Classification Society, 1, 11-22 (in Japanese).
  • Adachi, K. (2009). Joint Procrustes analysis for simultaneous nonsingular transformation of component score and loading matrices. Psychometrika, 74, 667-683.
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