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Data analysis and statistical inference based on permutation methods, EDF methods, bootstrap and resampling methods, kernel methods and Markov chain methods.
This course provides an introduction to nonparametric methods in statistics. We will learn how to estimate or test hypothesis about distribution functions, densities, and regression functions without assuming prior knowledge about the form of these functions. While the estimate of a standard linear regression model is always a linear regression function, the result of nonparametric regression can be any smooth function that provides a good fit to the data. Nonparametric methods often give better results for large samples but are computationally more demanding and have different data requirements than the standard methods. We will look into theoretical properties of nonparametric methods and learn how to apply these methods using R.
This course will:- Introduce the concepts of nonparametric estimation and testing- Introduce the error analysis of nonparametric methods- Introduce nonparametric data analysis with RYou will be able to:- Choose appropriate nonparametric methods for data analysis- Apply nonparametric methods using R- Understand and quantify the uncertainty of nonparametric methods- Interpret the results of nonparametric methods
STAT211, STAT213, STAT221, EMTH210, EMTH271 or at least B+ in (MATH103 or EMTH119).
Fabian Dunker
General information for students Library portal LEARN
Domestic fee $764.00
International fee $4,000.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Mathematics and Statistics .