401-0627-00L Smoothing and Nonparametric Regression with Examples
| Semester | Herbstsemester 2020 |
| Dozierende | S. Beran-Ghosh |
| Periodizität | jährlich wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
Lehrveranstaltungen
| Nummer | Titel | Umfang | Dozierende | ||||
|---|---|---|---|---|---|---|---|
| 401-0627-00 G | Smoothing and Nonparametric Regression with Examples The lecturers will communicate the exact lesson times of ONLINE courses. | 2 Std. |
| S. Beran-Ghosh |
Katalogdaten
| Kurzbeschreibung | 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. Examples from environmental research will be used for motivation, but the methods will also be applicable elsewhere. |
| Lernziel | 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. |
| Inhalt | Rough 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. |
| Skript | Brief summaries or outlines of some of the lecture material will be posted at https://www.wsl.ch/en/employees/ghosh.html. 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. |
| Literatur | References: - 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. |
| Voraussetzungen / Besonderes | Prerequisites: A background in Linear Algebra, Calculus, Probability & Statistical Inference including Estimation and Testing. |
Leistungskontrolle
| Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
| ECTS Kreditpunkte | 4 KP |
| Prüfende | S. Beran-Ghosh |
| Form | Sessionsprüfung |
| Prüfungssprache | Englisch |
| Repetition | Die Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich. |
| Prüfungsmodus | mündlich 30 Minuten |
| Zusatzinformation zum Prüfungsmodus | This is a closed book & closed notes exam. |
| Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan. | |
Lernmaterialien
| Hauptlink | Outlines and brief sketches of the lecture material will be posted here |
| Es werden nur die öffentlichen Lernmaterialien aufgeführt. | |
Gruppen
| Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
| Keine zusätzlichen Belegungseinschränkungen vorhanden. |
Angeboten in
| Studiengang | Bereich | Typ | |
|---|---|---|---|
| Statistik Master | Statistische und mathematische Fächer | W |  |
| Statistik Master | Fachbezogene Wahlfächer | W | |
| Umweltnaturwissenschaften Master | Methoden und Werkzeuge | W | |