Statistical inference for varying coefficient functions.

We consider models with varying coefficients, i.e. linear models in which the response and/or explanatory variables vary with another variable, for example time. These types of models can for example be used in HIV research, where the number of T-cells decreases over time and in addition depends on the number of T-cells at the time of infection. Moreover we study ordinary differential equations with varying coefficients that allow describing the dynamics of continuously changing processes. We estimate the varying coefficients by P-splines. This widely used sparse flexible smoothing technique has as an important advantage (over other smoothing techniques such as B-splines or smoothing splines) that the unknown functions can be modeled in a rich basis, while introducing sparsity by adding a penalty. The main aim of this project is to develop statistical methods that focus on qualitative features of the varying coefficients functions, e.g. whether a coefficient is really varying (in contrast to being constant) or whether it is a monotonic increasing function. Moreover we want to test general hypotheses concerning the coefficient functions, by exploiting the nice properties of P-splines such as its linearity in the basis functions.

Keywords:THEORETICAL STUDY, DIFFERENTIAL EQUATIONS, NONPARAMETRIC STATISTICS, METHODOLOGICAL RESEARCH

Disciplines:Analysis, Applied mathematics in specific fields, General mathematics, History and foundations, Statistics and numerical methods, Other mathematical sciences and statistics