Stat 992 (Spring 2013):
The 1st part of course description (covered by professor Jun Shao):
The first part of this course covers variable selection, classification, and estimation with high
dimensional data. Specific topics include traditional methods for variable selection, thresholding,
the LASSO, adaptive LASSO, group LASSO, sure independence screening, nonconcave
penalization, and Bayesian variable selection. A tentative schedule is:
Jan 22: Traditional variable selection
Jan 27: Traditional variable selection
Jan 29: Thresholding and penalization
Feb 3: High dimensional variable selection with deterministic covariates
Feb 5: Sure independence screening, LASSO, and improvements
Feb 10: Regularizing LASSO
Feb 12: Adaptive LASSO
Feb 17: Nonconcave penalized likelihood
Feb 19: Sparse LDA
Feb 24: BLASSO and Bayesian variable selection
Feb 26: Bayesian variable selection
March 3: Bridge estimators, group LASSO
March 5: Sure independence screening in generalized linear models
March 10: Tuning parameter selection
The 2nd part of course description (covered by professor Yazhen Wang):
The second part of this course introduces quantum statistics and quantum computation. Topics
include basic quantum physics and quantum probability, quantum statistics, quantum
computation, quantum information, and quantum simulation. A tentative schedule is:
Week 9: Introduction to Quantum computation
Week 10: Quantum probability and quantum statistics
Week 11: Quantum tomography
Week 12: Quantum compressed sensing
Week 13: Quantum simulation
Week 14: Quantum information
Week 15: Students' presentations
Requirements:
Each student taking this course for 3 credits is required to make a presentation based on a related
research paper, either in the middle of the semester (for the first part) or in the end of the
semester (for the second part).
Jan 22: Traditional variable selection
Jan 27: Consistency of variable selection
Jan 29: Thresholding and penalization
Feb 3: High dimensional covariance matrix estimation
Feb 5: Sparse LDA
Feb 10: High dimensional variable selection with deterministic covariates
Feb 12: Sure independence screening, LASSO
Feb 17: Regularizing LASSO
Feb 19: Adaptive LASSO*
Feb 24: BLASSO and Bayesian variable selection*
Feb 26: Group LASSO
March 3: Nonconcave penalized likelihood*
March 5: Tuning parameter selection*
March 10: Estimation of inverse of a covariance matrix*
March 12:
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9057409766 Song, Xinyu
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9069654540 Xie, Bingying
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9064738637 Yu, Yan
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9069909969 Zhang, Huikun
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