Lecture 2 (Thurs., Jan. 10): Begin Ch. 2: likelihood definition, discrete data likelihoods, multinomial likelihoods.
HW Assignment 1: problems 1.4, 1.8, 1.9, 1.12, 2.4, 2.11. Due Thurs., Jan. 17.
Lecture 3 (Tues., Jan. 15): Continuous data likelihoods, likelihoods with both discrete and continuous components, mixtures and censored data likelihoods, regression models.
Lecture 4 (Thurs., Jan. 17): Generalized linear models, marginal and conditional likelihoods.
HW Assignment 2: 2.13, 2.18, 2.21, 2.26, 2.33, 2.34 (since we didn't do 2.2, you may use muhat=4395.1, sigmahat=1882.5). Due Thurs., Jan. 24.
Lecture 5 (Tues., Jan. 22): Information matrix.
Lecture 6 (Thurs., Jan. 24): Computational methods: analytical, Newton iteration, EM algorithm (briefly), uniqueness of mle.
HW Assignment 3: 2.38, 2.45, 2.46, 2.58, 2.61, 3.1, 3.2. Due Thurs., Jan. 31.
Lecture 7 (Tues., Jan. 29): Ch. 3: Likelihood-based tests.
Lecture 8 (Thurs., Jan. 31): Likelihood-based tests continued, model adequacy, nonstandard testing situations.
HW Assignment 4: 3.5, 3.9, 3.11 (replace "two intervals" by "two lower bounds"), 3.14, 3.17 ( sas code), 3.20. Due Thurs., Feb. 7.
Lecture 9 (Tues., Feb. 5): Begin Ch. 4, Bayesian Methods. Normal Bayes models, Bayes estimators.
Lecture 10 (Thurs., Feb. 7): Credible intervals, conjugate priors, noninformative priors.
HW Assignment 5: 4.1, 4.2, 4.5, 4.7, 4.9, 4.20. Due Thurs., Feb. 14.
Lecture 11 (Tues, Feb. 12): Normal models with variance unknown.
Lecture 12 (Thurs., Feb. 14): Hierarchical and empirical Bayes.
HW Assignment 6: 4.11, 4.12, 4.13, 4.14, 4.15, 4.17a. Due Thurs., Feb. 21.
Lecture 13 (Tues., Feb. 19): Start Ch. 5. Overview and introduction of convergence with probability 1 and in probability.
Lecture 14 (Thurs., Feb. 21): Convergence in distribution. Relationships between modes of convergence.
HW Assignment 7: 5.2, 5.4, 5.5, 5.8, 5.11, 5.12, 5.15. Due Thurs., Mar. 14.
Lecture 15 (Tues., Feb. 26): Markov inequality. Continuity theorem.
Mid-Term, Thursday, Feb. 28, 10:00--11:40 (come early to class, and leave a little late). Covers Chapters 1-4. You may use one side of an 8.5 by 11 sheet of paper with hand-written notes during the exam. Calculators will not be needed.
Spring Break, March 4-8.
Lecture 16 (Tues., Mar. 12):"Big oh" O(.) and "little oh" o(.) notation, asymptotic normality, Delta Method theorems.
Lecture 17 (Thurs., Mar. 14):Slutsky's Theorem, approximation by averages.
HW Assignment 8: 5.19, 5.21, 5.27, 5.31, 5.33, 5.46, 5.48. Due Thurs., Mar. 21.
Lecture 18 (Tues., Mar. 19):Convergence in distribution for vectors (Cramer-Wold result), multivariate approximation by averages. CLT for double arrays.
Lecture 19 (Thurs., Mar. 21): Ch. 6: Consistency and asymptotic normality of maximum likelihood estimators. Asymptotic chi-squared convergence of test statistics.
HW Assignment 9: 5.37, 5.52, 5.53, 6.5, 6.7, 6.9 (assume also that I() is continuous), 7.1, 7.2a (maple makes this easy). Due Thurs., April 4.
Lecture 20 (Tues., March 26): Ch. 7: M-estimation introduction, basic theory.
No class March 28---Spring Holiday
Lecture 21 (Tues., April 2):M-estimation: the delta method, nonsmooth psi functions, regression
Lecture 22 (Thurs., April 4): Ch. 8: Misspecified models and the limiting distribution of Wald, score, and likelihood ratio statistics under misspecification.
HW Assignment 10: 7.6, 7.8, 7.11, 8.3, 8.4, 8.10. Due Thurs., April 11.
Lecture 23 (Tues., April 9): Brief comments on Monte Carlo studies (Ch. 9), and then introduction to the jackknife (Ch. 10).
Lecture 24 (Thurs., April 11): Jackknife continued.
HW Assignment 11: 9.1, 9.2, 10.2, 10.6, 10.7 ( r code, please choose your own seed, not 346), 10.9, 10.12, 10.15 Due Thurs., April 18.
Lecture 25 (Tues., April 16): Ch. 11: Introduction to the bootstrap.
Lecture 26 (Thurs., April. 18): Bootstrap II.
HW Assignment 11: 11.1, 11.3, 11.6 (perhaps make a histogram of the generated thetastar to help see what is happening) Some R code for the bootstrap., 11.8, 11.11, 11.14. Due Thurs., April 25.
Lecture 27 (Tues., April 23):Ch 12: Rank and permutation tests.
Lecture 28 (Thurs., April 25): Rank and permutation tests II.
Final Exam, Thursday, May 2, 8:00am--11:00am. Emphasizes Chapters 5-12. You may use both sides of an 8.5 by 11 sheet of paper with hand-written notes during the exam. Calculators will not be needed.