Statistics 7301

Schedule

This schedule is subject to revision. Students are expected to attend class meetings and to regularly check this page for updates to the schedule.

Date	Day	Lecture	Topic	Reading	Due
8/23	W	0	Overview	Keener 3.1
8/25	F	1	Statistical models	Keener 3.1
8/28	M	2	Decision theory	Keener 3.1
8/30	W	3	Sufficiency	Keener 3.2–3.3
9/1	F	4	Sufficiency and partitions
9/4	M		No Class
9/6	W	5	Minimal sufficiency	Keener 3.4	HW1
9/8	F	6	Exponential families	Keener 2
9/11	M	7	Exponential families	Keener 2
9/13	W	8	Exponential families	Keener 2	HW2
9/15	F	9	Exponential families	Keener 2
9/18	M	10	Convex loss functions	Keener 3.6
9/20	W	10	Convex losses (continued); Jensen’s inequality	Keener 3.6	HW3
9/22	F	11	Rao–Blackwell Theorem	Keener 3.5
9/25	M		Exam 1 (Lec 0-9, HW 1-3)
9/27	W	12	Completeness and ancillarity	Keener 3.5
9/29	F	13	Unbiased estimation	Keener 4.1
10/2	M	14	Fisher Information	Keener 4.5–4.6
10/4	W	15	Information inequality	Keener 4.5–4.6
10/6	F	16	Method of moments		HW4
10/9	M	17	Maximum likelihood
10/11	W	18	MLE in exponential families
		19	MLE in exponential families (online note)
10/13	F		No Class
10/16	M	20	Minimum contrast estimation		HW5
10/18	W	21	Overview of asymptotics	Keener 8.1
10/20	F		Exam 2 (Lec 10-19, HW4-5)
10/23	M	22	Consistency of MLE in expo families	Keener 8.3
10/25	W	23	Delta method	Keener 8.2,8.5,8.6
10/27	F	24	Delta method	Keener 8.2,8.5,8.6
10/30	M	25	Consistency of M-estimators	Keener 9.1	HW6
11/1	W	26	Consistency of M-estimators (ULLN)	Keener 9.1
11/3	F	26b	Consistency of M-estimators and MLE	Keener 9.2
11/6	M	27	Asymptotic normality of min contrast		HW7
11/8	W	28	Asymptotic normality and efficiency of MLE	Keener 9.3,9.7
11/10	F		No Class
11/13	M	30	Nonparametric estimation and the empirical CDF	Wasserman 2.1	HW8
11/15	W	31	Statistical functionals	Wasserman 2.2
11/17	F		Exam 3 (Lec 20 - 28, HW6-8)
11/20	M	32	Influence functions	Wasserman 2.3
11/22	W		No Class
11/24	F		No Class
11/27	M	33	Functional Delta method	Wasserman 2.3
11/29	W	34	Nonparametric density estimation	Wasserman 6.0	HW9
12/1	F	35	Asymptotic MISE of the histogram	Wasserman 6.2
12/4	M	36	Kernel density estimation	Wasserman 6.3
12/6	W	37	MISE bound for kernel density estimation	Wasserman 6.3	HW10
12/11	M		Final exam (10:00am – 11:45am)