Math 483 (Mathematical Statistics)

Syllabus
My schedule
Homework list
382 book
R stuff   A longer R intro
Book website



Week M W F
1 1.1 - 1.6 Probability review   Hw1 Probability review: conditional Probability review   Hw2
2 2: Random Variables   RV continued: transformations, CDF, joint distributions RV continued  
3 Labor Day   3: Expected values Expected values   Hw3
4 Expected values: MGFs   Chebyshev inequality and Ch.5 5: Convergence of RVs   Hw4
5 Ch.5: Theorem 5.5; Proof of CLT   Ch.5: S^2, proof of LLN   Ch.5: Delta method   Hw5
6 Estimates, T-distribution handout   Properties of estimates: Ch.6 Ch.7: empirical CDF
7 Ch 7: plug-in estimates   Review   key Midterm Exam   key
8 Ch 8: bootstrap (See R demo 8 under R stuff)   Ch.9: MOM   Hw6 49ers holiday
9 Ch 9: Max. Likelihood   Ch.9: MLE properties Ch.9: Fisher information   Hw7
10 Ch 9: F.I. examples (see R code for Cauchy)   Ch.9: Multivariate Normal and multipar. Fisher information Ch.9: multiparameter Fisher information, Delta-method   Hw8
11 Ch.9: Multiparameter Delta-method     Ch.9: Gamma example (see R code ) Ch.10: Hypothesis testing
12 Ch.10: Wald test   Hw9   Ch.10: Chi-square test (see Math382 textbook, Chapter 10.) Ch.10: LR test handout
13 Ch.10: LR test: multiparameter     Review for Exam 2 practice   key Exam 2   key
14 Ch.12: Bayesian inference     Ch.12: Bayesian inference   Hw10 Thanksgiving break
15 Ch.12: Bayesian inference     Ch.12: Bayesian inference     Ch.24: Gibbs sampler   Hw11
16 Ch.24: Gibbs sampler cont'd   (see R code )   Ch.13: Linear regression     Ch.13: Linear regression  


Maintained by Oleg Makhnin (olegm att nmt edu)