STAT40080 Nonparametric Statistics

Academic Year 2022/2023

The course covers both theory and practical applications of nonparametric methods. It serves to reinforce and provide a contrast to classical statistical methods in that no particular parametric distribution is assumed. Topics covered include: distribution-free statistics, statistics utilizing counting and ranking, permutation tests. These include commonly used statistics such as the Sign test, Wilcoxon Signed rank and Wilcoxon rank sum tests, goodness-of-fit tests such as the Anderson-Darling, Kruskal-Wallis and Friedman tests as well as topics such as Spearman's correlation coefficient. Modern methods of jackknife, bootstrap, density estimation and nonparametric regression (kernel, loess and spline) and smoothing will be covered. Not all material may be covered due to time constraints. The statistical software package R will be used. Typewritten notes for the course will be provided on Brightspace.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

On successful completion of this module students should be able to demonstrate familiarity with standard nonparametric methods. They should be able to carry out these procedures on data sets using the statistical software package R. They should be able to understand the difference between nonparametric methods and classical methods and have the knowledge to make informed judgements as to what method is appropriate in a given problem. Students will be familiar with bootstrap techniques and with permutation/randomization procedures. Students will be able to construct in addition to standard density estimators such as histograms, smooth kernel density estimators. Students will also be able to perform regression smoothing using loess and conduct kernel and spline regression

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

5
Computer Aided Lab

6
Specified Learning Activities

30
Autonomous Student Learning

48
Total

113
Approaches to Teaching and Learning:
Lectures, enquiry and problem-based learning. There will be alternating tutorials (five) and active task based learning in computer labs (six). 
Requirements, Exclusions and Recommendations
Learning Requirements:

A knowledge of probability and statistical inference to the level of Probability Theory STAT20110 and Inferential Statistics STAT20100 courses is required. Knowledge of calculus and linear algebra at First Science level is required.

Learning Recommendations:

Knowledge of linear models to the level of STAT30240 is desirable


Module Requisites and Incompatibles
Incompatibles:
STAT40330 - Nonparametric Statistics


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: End of trimester exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% Yes

100

Carry forward of passed components
No
 
Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Not yet recorded
Conover W (1999) Practical Nonparametric Statistics. 3rd edn. Wiley.

Gibbons J and Chakraborti S (2011) Nonparametric Statistical Inference. 4th
edn. CRC.

Hogg R & Craig A. (1978) Introduction to Mathematical Statistics. 4th edn.
Chapter 9. Macmillan Publishing Co, Inc.

Hollander M & Wolfe D (2012) Nonparametric Statistical Methods. 2nd edn.
Wiley.

Lehmann E (2006) Nonparametrics: Statistical Methods based on Ranks.
Springer.

Siegel S & Castellan NJ (1988) Nonparametric Statistics for the Behavioural
Sciences. 2nd edn. New York: McGraw-Hill.

Sprent P & Smeeton NC (2007) Applied Nonparametric Statistical Methods.
4th edn. CRC

Randles R & Wolfe D (1991) Introduction to the Theory of Nonparametric
Statistics. Wiley.

Wasserman, Larry (2007) All of nonparametric statistics. Springer.

Dalgaard Peter (2002). Introductory statistics with R. Springer.

Ugarte, Militino and Arnholt (2016). Probability and Statistics with R, Second
Edition. Chapman & Hall