Statistics and AI tools for Finance, Medicine, Business, and Smart City

ARTIFICIAL INTELLIGENCE

We simulate human intelligence in machines, and integrate artificial intelligence and statistical methods to invent new AI algorithms and models. We programme our invented methods into AI tools for information clustering, classification, grouping, relating, reasoning, and searching. We do consultancy with our invented AI tools for analyzing business's rules, processes and practices, for identifying the business risks and bottomnecks, for learning and capturing business's tacit knowledge into explicit knowledge, for predicting unforeseen problems, and for optimizing decision makings to enhance wisdow management of the businesses.

Machine Learning, Bootstrap,and High Dimensional Statistics

  • CHEN, J., YANG, H. and YAO, J.F. (2018). A new multivariate CUSUM chart using principal components with a revision of Crosier's chart. Communications in Statistics: Simulation and Computation, 47(2), 464-476.
  • FANG, Y., XU, J. and YANG, L. (2018). Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator. Journal of Machine Learning Research, 19:1-21.
  • LEE, M.S. and WU, Y. (2018). A bootstrap recipe for post-model-selection inference under linear regression models. Biometrika, 105(4), 873-890.
  • LI, W.M. and YAO, J.F. (2018). On structure testing for component covariance matrices of a high dimensional mixture. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 80(2), 293-318.
  • SHEN, K., YAO, J.F. and LI, W.K. (2018). On a spiked model for large volatility matrix estimation from noisy high-frequency data. Computational Statistics & Data Analysis, 131, 207-221.
  • WEI, B., LEE, M.S. and WU, X. (2016). Stochastically optimal bootstrap sample size for shrinkage-type statistics. Statistics and Computing, 26, 249-262.
  • SOLEYMANI, M. and LEE, M.S. (2014). Sequential combination of weighted and nonparametric bagging for classification. Biometrika, 101(2), 491-498.
  • XU, J. and YING, Z.L. (2010). Simultaneous estimation and variable selection in median regression using Lasso-type penalty. Annals of the Institute of Statistical Mathematics, 62(3), 487-514.
  • WANG, H.S., LI, G.D. and JIANG, G.H. (2007). Robust Regression Shrinkage and Consistent Variable Selection through the LAD-Lasso. Journal of Business and Economic Statistics, 25(3), 347-355.
  • WANG, H. S., LI, G.D. and TSAI, C.L. (2007). Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 69(1), 67-78.