CourseSpecifications
Valid as from the academic year 2024-2025

Statistical Inference (C004080)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
De Keersmaeker, Frederik

lectureseminar

WE02

Offered in the following programmes in 2024-2025
Master of Science in Statistical Data Analysis

lecturer-in-charge
crdts
6offeringA

Teaching languages
EnglishKeywords
Estimation theory, hypothesis testing
Position of the course
The students are introduced to the most important concepts and methods in the theory ofestimators and hypothesis testing and to the theoretical foundations of statistics. This training
enables the students to gain insight into the statistical methods used in the different domains
and applications of statistics. Students learn to develop inference methods to solve specificstatistical problems using this general theory.
Contents• Properties of estimators: bias, variance, mean squared error, asymptotic bias, consistency• Maximum Likelihood Estimators, Score vectors, Fisher information, the Cramer-Rao• inequality• Large sample inference: convergence in distribution and probability, the Weak Law of Large• Numbers, the Central Limit Theorem, Consistency of estimators, Slutsky's Lemma and the• Delta Method• Fundamentals of confidence intervals and hypothesis testing• Likelihood-based hypothesis testing: the Wald, Score and Likelihood ratio tests• Bayesian methods: Bayesian versus frequentist statistics, Bayes’ Rule, prior and posterior,• Bayesian estimation and credible intervals, conjugate priors• M-estimators: unbiased estimating equation, sandwich estimator of the variance• Inference under the bootstrap• Missing data: assumptions (missing completely at random, missing at random, missing not at• random), multiple imputation, data augmentation, multiple imputation by chained equations• (MICE)
Initial competences
Having successfully followed the courses Principles of Statistical Data Analysis and Analysis ofContinuous Data or having acquired the intended competences in another way.
Final competences
1 Have knowledge of the basics of statistical inference theory.
2 Apply in a specific context the most important estimation methods and inference techniques
1 of statistics.
3 Derive the relevant properties of estimators and test procedures.
4 Judge the applicability and limitations of statistical methods.
5 Use methods to solve practical statistical problems.
(Approved)16 Correctly interpret the properties of the methods in the specific context of the application.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methodsSeminar, Lecture
Study material
Type: Slides
Name: Slides made by lecturer
Indicative price: Free or paid by faculty
Optional: no
Available on Ufora : Yes
References
Wasserman, L. (2004). All of Statistics (1st Edition), Springer.
Casella G. en Berger, R.L. (2002). Statistical Inference, Duxbury Press.
Aronow, P. M., Miller B. T. (2019). Foundations of Agnostic Statistics (1st Edition), Cambridge.
Boos, D. and Stefanski, L. (2013) Essential Statistical Inference. Springer New York.
Course content-related study coaching
Interactive support via Ufora (e-mail) and personally after electronic appointment.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment
Examination methods in case of periodic assessment during the second examination period
Written assessment
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is not possible
Calculation of the examination mark
Periodical evaluation (75%) + non-periodical evaluation (25%).

(Approved)2