| CODE | SOR3242 | ||||||||
| TITLE | Robust Statistics | ||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 6 | ||||||||
| ECTS CREDITS | 2 | ||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||
| DESCRIPTION | Classical statistical data analysis and model fitting techniques rely heavily on a number of assumptions about the data being analysed or the process from which the data was generated. These assumptions aim at providing convenient mathematical rationalizations of an uncertain or complicated knowledge or belief. For these assumptions there exist sophisticated theories with important practical consequences which simplify greatly our computations. In this study unit we shall see that theoretical and computational convenience does not always deliver an adequate statistical technique. Measures of robustness and techniques for outlier detection will be discussed. We discuss location and scale estimators. Robust Inferential Statistics and Robust regression methods are also studied. Students also learn how to compute Robust Statistics using R software. Study-unit Aims: To show that the real world does not behave as nicely as described by assumptions taken in classical statistical techniques and therefore the results obtained by these procedures applied on real data applications can be very misleading. The robust approach to statistical modelling and data analysis aims at deriving methods that produce reliable parameter estimates and associated tests and confidence intervals, not only when the data follow a given distribution exactly, but also when this happens only approximately and when assumptions, such as those mentioned previously, are relaxed. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - appreciate the major results in this field as well as their use in practical applications; - compute measures of robustness which are used to choose robust methods for conducting inference; - compute outlier diagnostics and robust statistics using R software; - know the properties that one should look for when constructing new robust measures. 2. Skills By the end of the study-unit the student will be able to: - conduct statistical analysis using Robust statistical Techniques. Main Text/s and any supplementary readings: - Jurečková, J., & Picek, J. (2006). Robust Statistical Methods with R. Chapman and Hall/CRC, USA. - Maronna, R.A., Martin, R.D., & Yohai, V.J. (2006). Robust Statistics: Theory and Methods. John Wiley & Sons Ltd, West Sussex, England. - Rousseeuw, P.J., Leroy, A.M. (2003). Robust Regression and Outlier Detection. John Wiley& Sons, Inc., USA. - Hoaglin, D.C., Mosteller, F., & Tukey, J.W., (1983). Understanding Robust and Exploratory Data Analysis. John Wiley& Sons, Inc., USA. - Huber, P. J. (1981). Robust Statistics . John Wiley& Sons, Inc., USA. - Wilcox, R. (2013). Introduction to Robust Estimation and Hypothesis testing. Elsevier Inc. All. |
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| ADDITIONAL NOTES | Pre-requisite Study-units: SOR1110, SOR2120, SOR2221 and SOR2211. | ||||||||
| STUDY-UNIT TYPE | Lecture and Tutorial | ||||||||
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| LECTURER/S | Derya Karagoz |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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