HW Assignment 1: problems 1.1, 1.4, 1.9, 1.10, 2.1, 2.4, 2.9. Due Thurs., Sept. 4. Additional problems (not to be handed in): 1.8, 2.3, 2.5, 2.8.
Lecture 2 (Tues., Aug. 26): Begin Ch. 2, likelihood definition, discrete data likelihoods, multinomial likelihoods.
Lecture 3 (Thurs., Aug. 28): Continuous data likelihoods, likelihoods with both discrete and continuous components, mixtures and censored data likelihoods.
Lecture 4 (Tues., Sept. 2): Information matrix.
Lecture 5 (Thurs., Sept. 4): Regression models, generalized linear models.
Lecture 6 (Tues., Sept. 9): Marginal and conditional likelihoods.
HW Assignment 2: 2.33, 2.37, 2.38, 2.40, 2.18, 2.20. Due Thurs., Sept. 11. Additional problems (not to be handed in): 2.35, 2.14.
Lecture 7 (Thurs., Sept. 11): Computational methods: analytical, Newton iteration, and the EM algorithm.
HW Assignment 3: 2.24, 2.25, 2.43, 2.47, 2.52. Due Thurs., Sept. 18. Additional problems (not to be handed in): 2.54, 2.55.
Lecture 8 (Tues., Sept. 16): EM Algorithm continued, right censoring example. Appendix A: unique mle's.
Lecture 9 (Thurs., Sept. 18): Likelihood-based tests.
HW Assignment 4: 2.56, 2.57, 3.7, 3.11, 3.15, 3.17. Due Thurs., Sept. 25. Additional problems (not to be handed in): 3.5, 3.16.
Lecture 10 (Tues., Sept. 23): Likelihood-based tests continued.
Lecture 11 (Thurs., Sept. 25): Model adequacy and nonstandard testing situations.
HW Assignment 5: 3.20-3.23. Due Thurs., Oct. 2.
Lecture 12 (Tues., Sept. 30): Start large sample, Ch. 5. Overview and introduction of convergence with probability 1 and in probability.
Lecture 13 (Thurs., Oct. 2): Convergence in distribution. Relationships between modes of convergence.
Mid-Term Exam, Tuesday, Oct. 7, 6-9 p.m.
HW Assignment 6: 5.2, 5.3, 5.5, 5.8, 5.10. Due Thurs., Oct. 16. Additional problems (not to be handed in): 5.6, 5.12
Lecture 14 (Tues., Oct. 14): Markov inequality. Continuity theorem. "Big oh" O(.) and "little oh" o(.) notation.
Lecture 15 (Thurs., Oct. 16): Asymptotic normality, Delta Method theorems.
HW Assignment 7: 5.17, 5.21, 5.26, 5.30, 5.31. Due Thurs., Oct. 23. Additional problems (not to be handed in): 5.27, 5.29
Lecture 16 (Tues., Oct. 21): Slutsky's Theorem, approximation by averages. Sample central moments and sample percentiles. Finding h in the approximation by averages, influence curve.
Lecture 17 (Thurs., Oct. 23): More on finding h, convergence in distribution for vectors (Cramer-Wold result), multivariate approximation by averages.
HW Assignment 8: 5.23, 5.24, 5.33, 5.45, 6.2 (just verify conditions 1-4), 6.9. Due Thurs., Oct. 30. Additional problems (not to be handed in): 5.36, 6.6
Lecture 18 (Tues., Oct. 28): Ch. 6: Consistency and asymptotic normality of maximum likelihood estimators. Asymptotic chi-squared convergence of test statistics.
Lecture 19 (Thurs., Oct. 30): Ch. 7: M-estimation introduction, basic theory.
HW Assignment 9: 7.1, 7.2, 7.4, 7.7. Due Thurs., Nov. 6. Additional problems (not to be handed in): 7.6.
Lecture 20 (Tues., Nov. 4): M-estimation: examples with the delta method and nonsmooth psi functions.
Lecture 21 (Thurs., Nov. 6): Ch. 8: Misspecified models and the limiting distribution of Wald, score, and likelihood ratio statistics under misspecification.
HW Assignment 10: 7.9. 7.14, 8.7, 8.8, 8.10. Due Thurs., Nov. 13. Additional problems (not to be handed in): 8.4, 8.6.
Lecture 22 (Tues., Nov. 11): Generalized Wald and score statistics. Ch. 9: Monte Carlo simulation studies.
Lecture 23 (Thurs., Nov. 13): Ch. 10: Introduction to the jackknife.
HW Assignment 11: 9.4, 9.10, 10.2, 10.5, 10.6, 10.8. Due Tues., Nov. 25. Additional problems (not to be handed in): 9.13, 10.7
Lecture 24(Tues., Nov. 18): Jackknife II.
Lecture 25(Thurs., Nov. 20): Ch. 11: Introduction to the bootstrap.
Lecture 26(Tues., Nov. 25): Bootstrap II.
Thursday, Nov. 27 - No class - Thanksgiving.
Lecture 27(Tues., Dec. 2):Ch 12: Rank and permutation tests.
Lecture 28(Thurs., Dec. 4): Rank and permutation tests II.