Lecture 2 (Mon., Jan. 12): Forward and backward sequences. Mallows Cp and other linear model criteria.
Lecture 3 (Wed., Jan. 14): Mallows Cp and other linear model criteria.
Monday, January 19, 2009 is Martin Luther King Day. No classes.
Lecture 4 (Wed., Jan. 21): Derivation of AIC.
Homework 1 Due Wednesday, Feb. 4.
Lecture 5 (Mon., Jan. 26): Derivation of BIC.
Lecture 6 (Wed., Jan. 28): Asymptotic consistency in variable selection.
Lecture 7 (Mon., Feb. 2): Information-Reduction/Noise-Addition methods in variable selection. Lecturer: Dr. Stefanski.
Lecture 8 (Wed., Feb. 4): Noise Addition Variable Selection (NAMS).
Lecture 9 (Mon., Feb. 9): False Selection Rate (FSR) methods in variable selection.
Lecture 10 (Wed., Feb. 11): Adding quadratic terms (interactions and squares), hierarchy restrictions, and FSR.
Homework 2 Due Wednesday, Feb. 25.SAS Data set for Problem 5.
Lectures 11 (Mon., Feb. 16): Shrinkage/Penalty methods, Ridge regression, LASSO. Lecturer: Howard Bondell.
Lecture 12 (Wed., Feb. 18): Computer programs for the LASSO.
Lecture 13 (Mon., Feb. 23): Shrinkage/Penalty methods continued. Lecturer: Howard Bondell.
Lecture 14 (Wed., Feb. 25): Adaptive LASSO and the Least Squares Approximation (LSA) approach.
Spring Break - March 3-7, 2009.
Lecture 15 (Mon., March 9 ): Computing the adaptive LASSO using lars and the lsa software. Selecting lambda for the Adaptive LASSO .
Homework 3 Due Wednesday, March 25. Data set for problem 3.
Lecture 16 (Wed., March 11): Instability in variable selection methods and PIE measures.
Lecture 17 (Mon., March 16): Nonparametric variable selection using the COSSO. Lecturer: Helen Zhang.
Lecture 18 (Wed., March 18): Variable selection classification using support vector machine (SVM). Lecturer: Helen Zhang.
Lecture 19 (Mon., March 23): Ultra-high dimensional case, SIS and FAS.
Lecture 20 (Wed., March 18): Project description. Breakdown of variable selection, Donoho and Stodden (2006).
Lecture 21 (Mon., March 30): Bayesian variable selection.
Lecture 22 (Wed., April 1): Bayesian variable selection.
Lecture 23 (Mon., April 6): Inference after model selection.
Lecture 24 (Wed., April 8): Inference after model selection.
Lecture 25 (Mon., April 13): Longitudinal data, QIF approach.
Lecture 26 (Wed., April 15): QIF approach continued.