Google citations

Preprint

1. Imori, S., Katayama, S. and Wakaki, H. (2014)

   • Screening and Selection Methods in High-Dimensional Linear Regression Models

Print

1. Cho, S., Katayama, S., Lim, Y. and Choi, Y. G. (2021)

   • Positive definite modification of a covariance matrix by minimizing the matrix $\ell_{\infty}$ norm with applications to portfolio optimization

   • AStA Advances in Statistical Analysis

2. Katayama, S. (2019)

   • Computational and Statistical Analyses for Robust Non-convex Sparse Regularized Regression Problem

   • Journal of Statistical Planning and Inference, Vol 201, 20-31

3. Katayama, S., Fujisawa, H. and Drton, M. (2018)

   • Robust and sparse Gaussian graphical modeling under cell-wise contamination

   • Stat, Vol 7, Issue 1, e181. [Codes]

4. Katayama, S. and Fujisawa, H. (2017)

   • Sparse and Robust Linear Regression: An Optimization Algorithm and Its Statistical Properties

   • Statistica Sinica, Vol 27, 1243-1264

5. Katayama, S. and Imori, S. (2014)

   • Lasso Penalized Model Selection Criteria for High-Dimensional Multivariate Linear Regression Analysis

   • Journal of Multivariate Analysis. Vol 132, 138-150

6. Katayama, S. (2014)

   • High-Dimensional Mean Estimation via $\ell_1$-Penalized Normal Likelihood

   • Journal of Multivariate Analysis. Vol 130, 90-106

7. Katayama, S., Kano, Y., Srivastava, M. S. (2013)

   • Asymptotic Distributions of Some Test Criteria for the Mean Vector with Fewer Observations than the Dimension

   • Journal of Multivariate Analysis. Vol 116, 410-421. Ranked in the top 25 hottest articles (Jan to Mar 2013)

8. Srivastava, M.S., Katayama, S. and Kano, Y. (2012)

   • A Two Sample Test in High Dimensional Data

   • Journal of Multivariate Analysis. Vol 114, 349-358. Ranked in the top 25 hottest articles (Jan to Jun 2013)

9. Katayama, S. and Kano, Y. (2012)

   • A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix

   • Communications in Statistics -Theory and Methods-. Vol 43, 5290-5304