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This course is an introduction to nonparametric statistical methods based on empirical distribution functions, kernel smoothing, bootstrap, and resampling. We will study these methods by looking at their theoretical properties and their performance in practical data analysis
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.
15 points from 200 level MATH or EMTH, STAT210-299 or DATA203
Fabian Dunker
Domestic fee $824.00
International fee $4,750.00
* 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 .