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

SemesterAutumn Semester 2020
LecturersS. Beran-Ghosh
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-0627-00 GSmoothing and Nonparametric Regression with Examples
The lecturers will communicate the exact lesson times of ONLINE courses.
2 hrs
Fri10:00-12:00ON LI NE »
S. Beran-Ghosh

Catalogue data

AbstractStarting 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. Examples from environmental research will be used for motivation, but the methods will also be applicable elsewhere.
ObjectiveThe 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.
ContentRough Outline:

- Parametric estimation methods: selection of important results
o Maximum likelihood, Method of Least squares: regression & diagnostics

- Nonparametric curve estimation
o Density estimation, Kernel regression, Local polynomials, Bandwidth selection
o Selection of special topics (as time permits, we will cover as many topics as possible) such as rapid change points, mode estimation, robust smoothing, partial linear models, etc.

- Applications: potential areas of applications will be discussed such as, change assessment, trend and surface estimation, probability and quantile curve estimation, and others.
Lecture notesBrief summaries or outlines of some of the lecture material will be posted at Link.

NOTE: The posted notes will tend to be just sketches whereas only the in-class lessons will contain complete information.

LOG IN: In order to have access to the posted notes, you will need the course user id & the password. These will be given out on the first day of the lectures.
LiteratureReferences:
- 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.
- Kernel Smoothing: Principles, Methods and Applications, by S. Ghosh, Wiley.

Additional references will be given out in the lectures.
Prerequisites / NoticePrerequisites: A background in Linear Algebra, Calculus, Probability & Statistical Inference including Estimation and Testing.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersS. Beran-Ghosh
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 30 minutes
Additional information on mode of examinationThis 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

 
Main linkOutlines and brief sketches of the lecture material will be posted here
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Statistics MasterStatistical and Mathematical CoursesWInformation
Statistics MasterSubject Specific ElectivesWInformation
Environmental Sciences MasterMethods and ToolsWInformation