Latest news
Welcome to the course! The schedule for the course can be found in TimeEdit.
7/12: The first meeting to fix the schedule of the course is on Monday, January 15, 8:00 in MVH:11.
15/1: The course schedule is fixed, see TimeEdit and below. Exercises will be due every Thursday before the class and will be presented by the students during that class.
15/1: The student representatives of the course are: Oskar Allerbo (PhD students), Erik Håkansson (GU), Joel Sjögren (Chalmers)
12/2: Problem 4 on Assignment 4 was updated. The missing square was added. Please check the new version! Sorry for any inconvenience this might have caused and thank you to Oskar for spotting the typo!
19/2: Problem 1 on Assignment 5 was updated. Please add "nondecreasing" to the task.
Teachers
Course coordinator: Annika Lang
Course literature
Casella/Berger: Statistical Inference (2nd edition)
Hogg/McKean/Craig: Introduction to Mathematical Statistics (Person New International Edition 7e)
Jacod/Protter: Probability Essentials
Migon/Gamerman/Louzada: Statistical Inference: An Integrated Approach (2nd edition)
Wasserman: All of Statistics
Program
Lectures
Day 
Sections  Contents 

Mon 15/1 8  10 MVH11 
e.g. [JP] 
Introduction to random variables and expectations 
Wed 17/1 8  10 MVF31 
[CB] 5.1, 5.2 
Introduction to variance, independence, random samples,
statistics 
Thur 18/1 15  17 MVF31 
[CB] 5.2, 5.4, 6.2 [HMKC] 7.2 [MGL] 2.5 
Sample mean, sample variance, exponential families, order
statistics, sample median, distribution of order statistics,
sufficient statistics 
Mon 22/1 10  12 ML15 
[CB] 6.2 [HMKC] 7.2 [MGL] 2.5 
Factorization Theorem (FisherNeymanHalmosSavage), minimal
sufficeint statistics, LehmannScheffé Theorem 
Wed 24/1 8  10 MVF31 
[CB] 6.2, 6.3 [HMKC] 7.8, 7.9 [MGL] 2.5 
Ancillary and complete statistics, Basu's Theorem, likelihood
principle 
Thur 25/1 15  17 MVF31 

Discussion of Assignment 1 
Mon 29/1 10  12 ML15 
[CB] 7.2.1, 7.2.2 [HMKC] 6.1 [MGL] 4.3.1, 4.3.3 
Point estimation: method of moments, maximum likelihood
estimators 
Wed 31/1 8  10 MVF31 
[CB] 7.2.3, 7.2.4 [HMKC] 11.2.1, 11.2.2, 6.6 [MGL] 4.2, 5.2.2 
Point estimation: Bayes estimators, EM algorithm 
Thur 1/2 15  17 MVF31 
[CB] 7.3.1, 7.3.2 [HMKC] 7.1 [MGL] 4.5 
Discussion of Assignment 2, mean squared errors, best unbiased
estimators 
Mon 5/2 10  12 ML15 
[CB] 7.3.2, 7.3.3 [HMKC] 6.2, 7.3 [MGL] 4.5 
CramérRao inequality, RaoBlackwell Theorem 
Wed 7/2 8  10 MVF31 
[CB] 7.3.3, 7.3.4, 8.1, 8.2.1 [HMKC] 6.2, 7.1, 6.3 [MGL] 4.5, 4.1, 4.5, 6.1, 6.2 
Properties of best unbiased estimators, loss and risk functions,
introduction to hypothesis testing, likelihood ratio tests 
Thur 8/2 15  17 MVF31 
[CB] 8.2.1, 8.2.2 [HMKC] 6.3 [MGL] 6.2, 11.2.4 
Discussion of Assignment 3, LRT, Bayesian tests 
Mon 12/2 10  12 ML15 
[CB] 8.2.3, 8.3.1, 8.3.2 [HMKC] 8.1 [MGL] 6.2 
Unionintersection/intersectionunion tests, errors of Type I
and II, UMP tests, statement NeymanPearson lemma 
Wed 14/2 8  10 MVF31 
[CB] 8.3.2 [HMKC] 8.1 [MGL] 6.2.1, 6.2.2 
Proof NeymanPearson lemma, monotone likelihood ratios,
KarlinRubin theorem 
Thur 15/2 15  17 MVF31 
[CB] 8.3.4 [HMKC] 4.6 [MGL] 6.2 
Discussion of Assignment 4, pvalues 
Mon 19/2 10  12 ML15 
[CB] 9.1, 9.2.1, 9.2.2 [HMKC] 4.2 [MGL] 4.6 
Introduction to interval estimation, test inversion, pivoting 
Wed 21/2 8  10 MVF31 
[CB] 9.2.2, 9.2.3, 9.2.4 [HMKC] 4.2 [MGL] 4.6 
Pivoting, Bayesian set estimates 
Thur 22/2 15  17 MVF31 
[CB] 9.2.4, 9.3.1 [HMKC] 4.2 [MGL] 4.6 
Discussion of Assignment 5, Bayesian set estimates, evaluation
of interval estimates 
Mon 26/2 10  12 ML15 
[CB] 9.3.1, 9.3.2, 10 [HMKC] 4.2, 5 [MGL] 4.6, 5.3 
Evaluation of interval estimators, size and coverage
probabilities, testrelated optimality, asymptotic evaluations 
Wed 28/2 8  10 MVF31 
[CB] 10 [HMKC] 5 [MGL] 5.3 
Asymptotic evaluations 
Thur 1/3 15  17 MVF31 
Discussion of Assignment 6, conclusions 
Homework due
Day 
Exercises 

Thur 25/1 
Assignment 1 
Thur 1/2 
Assignment 2 
Thur 8/2 
Assignment 3 
Thur 15/2 
Assignment 4 
Thur 22/2 
Assignment 5 
Thur 1/3  Assignment 6 
Computer labs
Reference literature:
Learning MATLAB, Tobin A. Driscoll ISBN: 9780898716832 (The book is published by SIAM).
Course requirements
The learning goals of the course can be found in the course plan.
Assignments
Weekly assignments are given and due every Thursday. Students present the solutions in weekly exercise classes on Thursday and are requested to be able to present solutions for 75% of the exercises. The assignments consist of a combinations of theoretical exercises and programming tasks which can be implemented in the preferred programming language.
The handin of assignments works as follows:
 Send an email to annika.lang@chalmers.se no later than 14:30 on Thursday, where you give a list of solved exercises (on average not less than 75%).
 Come to the class on Thursday and be ready to present all exercises that you listed in the email at the blackboard. Bad presentations will not count as "presentation" and you will have to hand in a full corrected solution within a week that is distributed to all participants (else the problem will be deleted from your record of solved exercises and no model solution will be given).
 Two successful presentations are mandatory to pass.
Examination
Besides the assignments (2.5 hp) a written 4 hour exam (5 hp) has to be passed on Saturday, 17/3 in TBA. To pass the exam 50% of the points are required.
Examination procedures
In Chalmers Student Portal you can read about when exams are given and what rules apply on exams at Chalmers. In addition to that, there is a schedule when exams are given for courses at University of Gothenburg.
Before the exam, it is important that you sign up for the examination. If you study at Chalmers, you will do this by the Chalmers Student Portal, and if you study at University of Gothenburg, you sign up via GU's Student Portal, where you also can read about what rules apply to examination at University of Gothenburg.
At the exam, you should be able to show valid identification.
After the exam has been graded, you can see your results in Ladok by logging on to your Student portal.
At the annual (regular) examination:
When it is practical, a separate review is arranged. The date of the
review will be announced here on the course homepage. Anyone who can not
participate in the review may thereafter retrieve and review their exam
at the Mathematical
Sciences Student office. Check that you have the right grades and
score. Any complaints about the marking must be submitted in writing at
the office, where there is a form to fill out.
At reexamination:
Exams are reviewed and retrieved at the Mathematical
Sciences Student office. Check that you have the right grades and
score. Any complaints about the marking must be submitted in writing at
the office, where there is a form to fill out.