Contemporary Statistics with Applications (22/23)

 Faculteit Science and Engineering Jaar 2021/22 Vakcode WMMA015-05 Vaknaam Contemporary Statistics with Applications (22/23) Niveau(s) master Voertaal Engels Periode semester II a ECTS 5 Rooster tweejaarlijks, niet in 2019/2020

Uitgebreide vaknaam Contemporary Statistics with Applications (tweejaarlijks 2022/2023)
Leerdoelen The student is able to
1. apply a set of contemporary statistical methods to data. Those methods include: Linear methods for regression, Discriminant Analysis, Cubic Splines, Model Assessment and Selection Criteria, Additive Models, Boosting Approaches, Cross-Validation, Randomization Tests, the Expectation-Maximisation Algorithm and Hidden Markov Models.
2. derive elementary properties for the following methods: Linear methods for regression, Discriminant Analysis, Cubic Splines, Model Assessment and Selection Criteria, Additive Models, Boosting Approaches, Cross-Validation, Randomization Tests, the Expectation-Maximisation Algorithm and Hidden Markov Models.
Omschrijving New measurement technologies in recent years have generated new data structures, which demand new forms of statistical analysis. High-dimensional data have become a feature of many quantitative sciences, as often it is very cheap to measure lots of things on the same statistical subject. Astronomists scan interstellar objects in many different wavelengths, microarrays measure the expression of thousands of genes on single sample, etc.. In such cases offer sparse inference techniques ways to overcome the large p, small n dilemma.
Uren per week
Onderwijsvorm Hoorcollege (LC), Opdracht (ASM), Practisch werk (PRC), Werkcollege (T)
(Weekly twice two-hour lectures and one two-hour practical. The practical will be held in a computer lab and involves programming in R.)
Toetsvorm Opdracht (AST), Verslag (R)
(Assessment takes place through three homework assignments and a written research project according to the formula: Final = 0.1 x max(HW1, R) + 0.1 x max(HW2, R) + 0.1 x max(HW3, R) + 0.7 x R, where HW1, HW2 and HW3 are the homework grades for 1st, 2nd, and 3rd homework and R = grade of the written project report.)
Vaksoort master
Coördinator prof. dr. M.A. Grzegorczyk
Docent(en) prof. dr. M.A. Grzegorczyk
Verplichte literatuur
Titel Auteur ISBN Prijs
The Elements of Statistical Learning
Data Mining, Inference, and Prediction, 2nd ed., 2009, XXII, 746 p., Hardcover
Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome 978-0-387-84857-0
Entreevoorwaarden The course unit assumes prior knowledge acquired from the two course units Probability Theory and Statistics of the Mathematics BSc Programme.
Opmerkingen This course was registered last year with course code WICSA-10
Opgenomen in
Opleiding Jaar Periode Type
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