HW Assignment 1: problems 1.2, 1.5, 1.9, 1.10, 2.1, 2.3, 2.5, 2.6. Due Thurs., Sept. 3. Additional problems (not to be handed in): 1.8, 2.4, 2.8.
Lecture 2 (Tues., Aug. 25): Begin Ch. 2, likelihood definition, discrete data likelihoods, multinomial likelihoods.
Lecture 3 (Thurs., Aug. 27): Continuous data likelihoods, likelihoods with both discrete and continuous components, mixtures and censored data likelihoods.
Lecture 4 (Tues., Sept. 1): Regression models, generalized linear models.
Lecture 5 (Thurs., Sept. 3): Marginal and conditional likelihoods.
HW Assignment 2: 2.12, 2.13, 2.15, 2.18, 2.24, 2.26. Due Thurs., Sept. 10. Additional problems (not to be handed in): 2.20, 2.22.
Lecture 6 (Tues., Sept. 8): Information matrix.
Lecture 7 (Thurs., Sept. 10): Information matrix continued, computational methods: analytical, Newton iteration, and the EM algorithm.
HW Assignment 3: 2.37, 2.38, 2.43, 2.46, 2.49. Due Thurs., Sept. 17. Additional problems (not to be handed in): 2.54, 2.55.
Lecture 8 (Tues., Sept. 15): EM Algorithm continued, right censoring example. Appendix A: unique mle's.
Lecture 9 (Thurs., Sept. 17): Likelihood-based tests, part 1.
HW Assignment 4: 2.57, 2.58, 3.2, 3.8, 3.11, 3.19 (sas code). Due Thurs., Sept. 24. Additional problems (not to be handed in): 3.5, 3.15.
Lecture 10 (Tues., Sept. 22): Likelihood-based tests continued.
Lecture 11 (Thurs., Sept. 24): Model adequacy and nonstandard testing situations.
HW Assignment 5: 3.13, 3.17, 3.21, 3.22. Due Thurs., Oct. 1. Additional problems (not to be handed in): 3.20.
Lecture 12 (Tues., Sept. 29): Start large sample, Ch. 5. Overview and introduction of convergence with probability 1 and in probability.
Lecture 13 (Thurs., Oct. 1): Convergence in distribution. Relationships between modes of convergence.
Mid-Term Exam, Tuesday, Oct. 6, 10:00 - 11:45 a.m. (come early to class, and leave a little late). Covers Chapters 1-3. 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.
Lecture 14 (Tues., Oct. 13): Markov inequality. Continuity theorem.
Lecture 15 (Thurs., Oct. 15):Continuity theorem continued.
HW Assignment 6: 5.3, 5.4, 5.9, 5.10, 5.15, 5.18, 5.21. Due Thurs., Oct. 22. Additional problems (not to be handed in): 5.12, 5.20.
Lecture 16 (Tues., Oct. 20):"Big oh" O(.) and "little oh" o(.) notation, asymptotic normality, Delta Method theorems.
Lecture 17 (Thurs., Oct. 22):Slutsky's Theorem, approximation by averages.
HW Assignment 7: 5.25, 5.26, 5.29, 5.36, 5.44. Due Thurs., Oct. 29. Additional problems (not to be handed in): 5.27, 5.31
Lecture 18 (Tues., Oct. 27): Sample central moments and sample percentiles. Finding h in the approximation by averages, influence curve.
Lecture 19 (Thurs., Oct. 29):Convergence in distribution for vectors (Cramer-Wold result), multivariate approximation by averages.
HW Assignment 8: 5.45, 5.46, 5.47, 5.50 Due Thurs., Nov. 5. Additional problems (not to be handed in): 5.48, 5.51
Lecture 20 (Tues., Nov. 3): Ch. 6: Consistency and asymptotic normality of maximum likelihood estimators. Asymptotic chi-squared convergence of test statistics.
Lecture 21 (Thurs., Nov. 5): Ch. 7: M-estimation introduction, basic theory.
HW Assignment 9: 6.1, 6.5, 7.1, 7.3, 7.7. Due Thurs., Nov. 12. Additional problems (not to be handed in): 7.2, 7.4
Lecture 22 (Tues., Nov. 10): M-estimation: examples and the delta method.
Lecture 23 (Thurs., Nov. 12): M-estimation: nonsmooth psi functions and regression.
HW Assignment 10: 7.8. 7.11, 7.12, 7.13. Due Thurs., Nov. 19. Additional problems (not to be handed in): 7.6, 7.14.
Lecture 24 (Tues., Nov. 17): Ch. 8: Misspecified models and the limiting distribution of Wald, score, and likelihood ratio statistics under misspecification.
Lecture 25 (Thurs., Nov. 19): Generalized Wald and score statistics.
Lecture 26 (Tues., Nov. 24): Ch. 10: Introduction to the jackknife.
HW Assignment 11: 8.1, 8.3, 8.9, 10.3, 10.5, 10.6. Due Thurs., Dec. 3. Additional problems (not to be handed in): 8.4, 8.10.
Lecture 27(Tues., Dec. 1): Jackknife II.
Lecture 28(Thurs., Dec. 3): Ch. 11: Introduction to the bootstrap.