Mondays: 12.15 - 14.00 in room R4.
Tuesdays: 08:15-10:00 in room B3.
Fridays: 14-15
Wednesdays 08:15-09:00 in room B3. First time 05.09. Note that the exercises are not compulsory in the sense
that they will be collected and approved, but they are extemely important for the exam.
Exercise 1, 05.09: 2.33ac, 2.35, 2.38, 3.28abd, 3.39. lf
Exercise 2, 12.09: Exercise 2 lf
Exercise 3, 19.09: 3,46, 3.47 (hard), 4.1, 4.4, 4.10, 4.34. lf
Exercise 4: 26.09: 4.15, 4.30, 4.31, 4.32, 4.35, 4.36, 4.58 lf
Exercise 5: 03.10: 5.6, 5.17, 5.31, 5.35 lf
Exercise 6: 10.10: 5.36, 5.43a), 5.44, 6.1. lf
Exercise 7: Exercise 7
lf
Exercise 8: Exercise 8 Note of Rue and Skaflestad
Bayes and the normal distribution in details
lf
Exercise 9: Exercise 9. lf
Exercise 10: Exercise 10.
lf
Exercise 11: Exercise 11.
lf
Exercise 12: Exercise 12 lf
John Tyssedal, room 1132, SII. Email: John [dot] tyssedal [at] ntnu [dot] no
Jacopo Paglia, room 1001, SII. Email: jacopo [dot] paglia [at] ntnu [dot] no
The curriculum is defined to be all that is covered in lectures and exercises, as described on the course homepage under "Lecture plan and progress" and "Exercises".
The following list gives references to the planned topics covered from the course book: Statistical Inference by George Casella and Roger Berger (Second Edition)
Chapter 1: Probability theory. Assumed known
Chapter 2: Transformations and expectations. 2.1 (assumed known); 2.2-2.4
Chapter 3: Common families of distributions. 3.1-3.3 (assumed known); 3.4, 3.5, 3.6.1
Chapter 4: Multiple random variables. 4.1-4.6 (4.5 only partly); 4.7 (only Cauchy-Schwarz and Jensen's inequality)
Chapter 5: Properties of a random sample. 5.1-5.3 (not all in detail),5.5.1, 5.5.3, 5.5.4.
Chapter 6: Principles of data reduction. 6.1, 6.2.1, 6.2.2, Def. 6.2.21, Examples 6.22.22-23, Theorem 6.2.25
Chapter 7: Point estimation. 7.1, 7.2.1, 7.2.2, 7.2.3, 7.3.1 (except from "In certain situations…" on p. 333), 7.3.2, 7.3.3 (from beginning to Example 7.3.18, then from top of page 347 and rest of section 7.3.3; Theorem 7.5.1.
Chapter 8: Hypothesis testing. 8.1, 8.2.1, 8.3.1 (except page 387), 8.3.2 until Def. 8.3.16 (only part a in Theorem 8.3.12).
Chapter 9: Interval estimation. 9.1, 9.2
Chapter 10: Asymptotic evaluations. 10.1.1, 10.1.2, 10.1.3, 10.3.1 to theorem 10.3.3, Score statistic on page 494.
The exam will be on December 5., 9.00-13.00. It will be a written exam.
You are allowed to bring with you:
Tabeller og formler i statistikk
NTNU certified calculator
Personal, handwritten, stamped yellow sheet, A5-format. You get the sheet in the Department office, 7. floor
The exam text will contain a collections of results from the text-book as given here
Earlier exams with solutions can be found here:earlier exams
The exam 2017 with suggested solution
The exam 2018 with suggested solution
The curriculum should make you able to solve the problems in most of the earlier exams, but you will meet questions about ARE or Square Loss function in a Bayesian setting in some of them. That is not covered by our curriculum, and you will not meet such questions on the exam.
Meeting hours before the exam in my offfice
Friday 30. November 14-16
Monday 3. December 13-15
Tuesday 4. December 13-15