Sampling and Estimation
7.5 credits cr.
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Sampling methodology is about drawing representative samples from a finite population and how to best use existing information. In this course you will learn about the most commonly used sampling designs.
This course gives you useful knowledge about planning and assessing different types of survey designs. You will learn to explain the advantages and disadvantages of standard sampling designs, how to choose appropriate sampling designs for different selection problems and how to choose suitable estimators depending on the problem and the access to auxiliary information. You will be able to carry out estimation and precision estimation on data from different sampling designs, with and without auxiliary information and to describe and use common estimation methods for non-response problems, including imputation and calibration. The course treats several different sampling methods and different estimation procedures are discussed.
The methods you will learn through the course are: simple random sampling, stratified sampling, sampling with varying inclusion probabilities (known as πps-sampling), cluster sampling, multistage sampling and systematic sampling. Within the course you will discuss how to choose between methods and designs and how to implement them. The course also discusses different estimation procedures, particularly when there are different types of auxiliary information in the frame.
You will also get insights about the literature and scientific publications on the field.
The course is given at day time, full time.
The course forms a part of the Master's Program in Statistics, but it can also be studied as a freestanding course.
The teaching forms consist of lectures and exercises.
Examination will be in the form of written and oral examination.
Course literatureNote that the course literature can be changed up to two months before the start of the course.
If you have questions about studying at the Department of Statistics, please contact our study- and career counselor.