DescriptionLinear regression is one of the most commonly used techniques in statistics. In the most simple case one has bivariate data pairs (x_i,y_i) and tries to fit a line of type y=a+bx through the data. However, the actual relationship between predictor and response is often far from linear (think about stock rates, for example) and linear regression methods are then inadequate. To some extent, one can solve this problem by employing alternative parametric functions, e.g. a parabola, but these models are not very flexible and require a large number of parameters if the data structure is quite complex.The better approach in those cases is to use nonparametric regression methods, which allow for arbitrary 'smooth' functions and do not require to predefine a parametric regression function. As these methods summarize the data by a smooth curve, they are often referred to as 'smoothing' methods. Today exist a large number of well established smoothing methods, which find frequent application in all kind of disciplines, e.g. natural and environmental sciences, actuarial sciences, and medicine (returning to the stock rates application, a simple and often applied smoother is here the `moving average', which is a not very sophisticated smoothing method). In this project you will learn how smoothing techniques work, get an impression of their theoretical background, and apply them on real data sets using modern statistical software packages (e.g. R).
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email: jochen.einbeck "at" durham.ac.uk