##--------------------- as of 4/18/2022 SOME PROJECT PAPER POSSIBILITIES RELATED TO EM, FACTOR MODELS AND BOOTSTRAP EM, Kernel Techniques Rubin and Thayer (1982) paper Christopher Bishop book Chapters 6, 12 SPARSE PCA 1. Paper with good introduction to sparse PCA, penalty methods with good references: Jong-Hoon Ahn and Jong-Hoon Oh (2003), A Constrained EM Algorithm for Principal Component Analysis Neural Computation 15, 57-65. https://doi.org/10.1162/089976603321043694 2.Penalized simultaneous components analysis. The key references are all mentioned. https://link.springer.com/article/10.3758/s13428-018-1163-z . 3. Haipeng Shen and Jianhua Z. Huang (2008 Sparse principal component analysis via regularized low rank matrix approximation, Journal of Multivariate Analysis 99, 1015 – 1034 4. Hui Zou, Trevor Hastie and Robert Tibshirani (2006), Sparse Principal Component Analysis, Jour. Computational and Graphical Stat. 15, 265-286. BOOTSTRAP (Parametric Bootstrap versus Nonparametric) Among references given in STAT818D web-page http://www.math.umd.edu/~evs/s818D: Chapter in Das Gupta, A. (2008), Chapter 29 on The Bootstrap in: Asymptotic Theory of Statistics and Probability, Springer. LASSO (a technique for variable selection that threshols small refression coeff's to 0) FOR MULTIVARIATE REGRESSION Yanming Li, Bin Nan, and Ji Zhu (2015), Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure, Biometrics. 2015 71, 354–363.