Fall 2019 - STAT 852 G100
Modern Methods in Applied Statistics (4)
Class Number: 4608
Delivery Method: In Person
An advanced treatment of modern methods of multivariate statistics and non-parametric regression. Topics may include: (1) dimension reduction techniques such as principal component analysis, multidimensional scaling and related extensions; (2) classification and clustering methods; (3) modern regression techniques such as generalized additive models, Gaussian process regression and splines.
- Problems with high dimensions,
- Variable selection: stepwise, shrinkage, LASSO, and penalized likelihood
- Modern regression techniques: Splines, trees, generalized additive models
- Ensemble learning methods
- Classification and clustering methods
- Dimension reduction techniques: Principal components and multidimensional scaling
- Assignments 50%
- Projects 50%
Above grading is subject to change.
Final Presentations will be held: Dec 9th, 10:30-3:30 in K9509
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.) by Trevor Hastie, Robert Tibshirani, Jerome Friedman. Publisher: Springer
Book is available on-line through the SFU Library
eBook: ISBN 978-0-387-84858-7
Hardcover: ISBN 978-0-387-84857-0
Modern Multivariate Statistical Analysis: Regression, Classification, and Manifold Learning. by Alan J. Izenman. Publisher: Springer
Book is available for free on-line through the SFU Library
eBook: ISBN 978-0-387-78189-1
Hardcover: ISBN 978-0-387-78188-4
Graduate Studies Notes:
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