Google citations

Preprint

1. Imori, S., Katayama, S. and Wakaki, H. (2014)
   • Screening and Selection Methods in High-Dimensional Linear Regression Models

Print

1. Katayama, S. (2018)
   • Computational and Statistical Analyses for Robust Non-convex Sparse Regularized Regression Problem
   • Journal of Statistical Planning and Inference, Vol 201, 20-31
2. 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]
3. Katayama, S. and Fujisawa, H. (2017)
   • Sparse and Robust Linear Regression: An Optimization Algorithm and Its Statistical Properties
   • Statistica Sinica, Vol 27, 1243-1264
4. 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
5. Katayama, S. (2014)
   • High-Dimensional Mean Estimation via $\ell_1$-Penalized Normal Likelihood
   • Journal of Multivariate Analysis. Vol 130, 90-106
6. 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)
7. 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)
8. 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