REFERENCES
- Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in 2nd International Symposium on Information Theory, eds. B. N. Petrov and F. Csaki, Budapest: Akademia Kiado, pp. 267–281.
- Bickel, P.J., and Levina, E. (2004), “Some Theory for Fisher's Linear Discriminant Function, ‘naïve Bayes,’ and Some Alternatives When There are Many More Variables than Observations,” Bernoulli, 10, 989–1010.
- ——— (2008), “Regularized Estimation of Large Covariance Matrices,” The Annals of Statistics, 36, 199–227.
- Cai, T.T., and Liu, W. (2011), “A Direct Estimation Approach to Sparse Linear Discriminant Analysis,” Journal of the American Statistical Association, 106, 1566–1577.
- Chen, J., and Chen, Z. (2008), “Extended Bayesian Information Criterion for Model Selection with Large Model Spaces,” Biometrika, 95, 759–771.
- Clemmensen, L., Hastie, T., and (2011), “Sparse Discriminant Analysis,” Technometrics, 53, 406–413.
- Fan, J., and Fan, Y. (2008), “High Dimensional Classification Using Features Annealed Independence Rules,” The Annals of Statistics, 36, 2605–2637.
- Fan, J., Feng, Y., and Song, R. (2011), “Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models,” Journal of the American Statistical Association, 116, 544–557.
- Fan, J., Feng, Y., and Tong, X. (2012), “A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant,” Journal of the Royal Statistical Society, Series B, 74, 745–771.
- Fan, J., and Lv, J. (2008), “Sure Independence Screening for Ultra-High Dimensional Feature Space” (with discussion), Journal of the Royal Statistical Society, Series B, 70, 849–911.
- Fang, K.T., Li, R., and Liang, J. (1998), “A Multivariate Version of Ghosh's MT3 Plot to Detect Non-multinormality,” Computational Statistics and Data Analysis, 28, 371–386.
- Guo, Y., Hastie, T., and Tibshirani, R. (2007), “Regularized Discriminant Analysis and Its Application in Microarrays,” Biostatistics, 1, 86–100.
- Liang, J.J., Li, R., Fang, K.T., and Fang, H.B. (2000), “Testing Multinormality Based on Low-dimensional Projection,” Journal of Statistical Planning and Inference, 86, 129–141.
- Mai, Q., Zou, H., and Yuan, M. (2012), “A Direct Approach to Sparse Discri-minant Analysis in Ultra-high Dimensions,” Biometrika, 29–42.
- Mardia, K.V. (1970), “Measures of Multivariate Skewness and Kurtosis with Applications,” Biometrika, 69, 519–530.
- Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461–464.
- Shao, J., Wang, Y., Deng, X., and Wang, S. (2011), “Sparse Linear Discriminant Analysis by Thresholding for High Dimensional Data,” The Annals of Statistics, 39, 1241–1265.
- Tibshirani, R., Hastie, T., Narashimhan, B., and Chu, G. (2003), “Class Prediction by Nearest Shrunken Centroids with Applications to DNA Microarrays,” Statistical Science, 18, 104–117.
- Wang, H. (2009), “Forward Regression for Ultra-high Dimensional Variable Screening,” Journal of the American Statistical Association, 104, 1512–1524.
- ——— (2012), “Factor Profiled Independence Screening,” Biometrika, 99, 15–28.
- Wang, H., Li, R., and Tsai, C.L. (2007), “Tuning Parameter Selectors for the Smoothly Clipped Absolute Deviation Method,” Biometrika, 94, 553–568.
- Weiss, S.M., Indurkhya, N., Zhang, T., and Damerau, F.J. (2005), Text Mining: Predictive Methods for Analyzing Unstructured Information, New York: Springer.
- Witten, D.M., and Tibshirani, R. (2011), “Penalized Classification Using Fisher's Linear Discriminant,” Journal of the Royal Statistical Society, Series B, 73, 753–772.
- Zhu, L.P., Li, L., Li, R., and Zhu, L.X. (2011), “Model-Free Feature Screening for Ultrahigh Dimensional Data,” Journal of the American Statistical Association, 106, 1464–1475.