401-0627-00L  Smoothing and Nonparametric Regression with Examples

Courses

Catalogue data

Abstract	Starting with an overview of selected results from parametric inference, kernel smoothing will be introduced along with some asymptotic theory, optimal bandwidth selection, data driven algorithms and some special topics. Selected numerical examples will be used for motivation. The presented methods will also be applicable elsewhere.
Learning objective	The students will learn about methods of kernel smoothing and application of concepts to data. The aim will be to build sufficient interest in the topic and intuition as well as the ability to implement the methods to various different datasets.
Content	Rough Outline: - Parametric estimation methods: selection of important results o Method of Least squares: regression & diagnostics - Nonparametric curve estimation o Density estimation, Kernel regression, Local polynomials, Bandwidth selection, various theoretical results related to consistency o Selection of special topics (as time permits, we will discuss some of the following): rapid change points, mode estimation, partial linear models, probability and quantile curve estimation, etc. - Applications: potential areas of applications will be discussed such as, change assessment, trend and surface estimation and others.
Lecture notes	Summaries or outlines of some of the lecture material may be communicated to registered students by Email at irregular intervals. Note: These summaries/outlines will tend to be brief, likely to be incomplete & may have typos. Only in-class lessons will contain complete information.
Literature	References: - Kernel Smoothing: Principles, Methods and Applications, by Sucharita Ghosh, Wiley. - Statistical Inference, by S.D. Silvey, Chapman & Hall. - Regression Analysis: Theory, Methods and Applications, by A. Sen and M. Srivastava, Springer. - Density Estimation, by B.W. Silverman, Chapman and Hall. - Nonparametric Simple Regression, by J. Fox, Sage Publications. - Applied Smoothing Techniques for Data Analysis: the Kernel Approach With S-Plus Illustrations, by A.W. Bowman, A. Azzalini, Oxford University Press. Additional references will be given out in the lectures.
Prerequisites / Notice	Prerequisites: A background in Linear Algebra, Calculus, Probability & Statistical Inference including Estimation and Testing.
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making assessed Media and Digital Technologies fostered Problem-solving assessed Social Competencies Communication fostered Personal Competencies Adaptability and Flexibility fostered Creative Thinking assessed Critical Thinking assessed Integrity and Work Ethics fostered Self-awareness and Self-reflection fostered Self-direction and Self-management fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	4 credits
Examiners	S. Beran-Ghosh
Type	session examination
Language of examination	English
Repetition	The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination	oral 30 minutes
Additional information on mode of examination	This is a closed book & closed notes exam.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
Statistics Master	Subject Specific Electives	W
Environmental Sciences Master	Methods and Tools	W