Instructor Office Hours: Monday, Thursday 11:00 am -12:30 pm (Online office hours, Zoom link will be sent the day before)
TA Office Hours: Zeyu Yuwen (zeyu.yuwen@ufl.edu) Tuesday, 12 pm – 2 pm (Online office hours, Zoom link will be sent the day before)
Exam schedule:
Exam 1: Tuesday, February 11
Exam 2: Wednesday, March 25
Exam 3: Wednesday, April 22
Homework: Homeworks, Practice problems, and solutions will be sent via the class email list
Running list of topics covered in class
Monday, January 6: General statistical framework, fundamental principle of statistics
Wednesday, January 8: Sufficiency, Factorization theorem
Friday, January 10: Method of moments
Monday, January 13: Maximum likelihood estimation – definition
Wednesday, January 15: Maximum likelihood estimation – examples
Friday, January 17: Maximum likelihood example – linear model (derivation of MLE)
Wednesday, January 22: Maximum likelihood – linear model (derivation of distribution of MLE)
Friday, January 24: Maximum likelihood – linear model (derivation of distribution of MLE)
Monday, January 27: EM algorithm – Normal mixture example
Wednesday, January 29: EM algorithm – General form
Friday, January 31: Invariance of the MLE
Monday, February 3: Convergence of the EM algorithm
Wednesday, February 5: EM algorithm – Probit regression
Friday, February 7: Bayesian statistics – Introduction
Monday, February 10: Bayesian statistics – Examples
Wednesday, February 12: Bayesian statistics – Data Augmentatin (DA) algorithm
Friday, February 14: DA algorithm – Probit regression example
Monday, February 17: DA algorithm – Normal mixture example
Wednesday, February 19: Uniformly Minimum Variance Unbiased Estimation (UMVUE) – Introduction
Friday, February 21: UMVUE – Function of complete, sufficient statistic
Monday, February 24: UMVUE – Conditioning on complete, sufficient statistics
Friday, February 26: UMVUE – Proofs
Monday, March 9: Cramer-Rao inequality
Wednesday, March 11: Cramer-Rao inequality (attainment of lower bound)
Friday, March 13: Hypothesis testing – Introduction
Monday, March 16: Likelihood ratio test
Wednesday, March 18: Likelihood ratio test – further examples
Friday, March 20: Size of a test and choice of cutoff for the rejection region
Monday, March 23: Randomized test functions, Binomial LRT
Friday, March 27: Power function and Most Powerful tests
Monday, March 30: Neyman-Pearson lemma – proof
Wednesday, April 1: Neyman-Pearson lemma – examples
Friday, April 3: Karlin-Rubin theorem, p-value
Monday, April 6: Confidence regions
Wednesday, April 8: Revision of convergence concepts
Friday, April 10: Methods of establishing consistency
Monday, April 13: Consistency of MLE
Wednesday, April 15: Methods of establishing asymptotic normality
Friday, April 17: Asymptotic normality of MLE and Lyapunov CLT