Functional data analysis (FDA) is an active field of statistics, in which the primary subjectsin the study are curves. My dissertation consists of two innovative applications offunctional data analysis in biology. The data that motivated the research broadened thescope of FDA and demanded new...

This dissertation consists of five independent projects. In each project, a novelstatistical method was developed to address a practical problem encountered in genomiccontexts. For example, we considered testing for constant nonparametric effectsin a general semiparametric regression model in...

Semiparametric regression has become very popular in the field of Statistics over theyears. While on one hand more and more sophisticated models are being developed,on the other hand the resulting theory and estimation process has become more andmore involved. The main problems that are...

Motivated by a nonparametric GARCH model we consider nonparametric additive regression and autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can be estimated with parametric rate and give the normal limit. Our procedure...

We describe methods for estimating the regression function nonparametrically and for estimating the variance components in a simple variance component model which is sometimes used for repeated measures data or data with a simple clustered structure. We consider a number of different ways of...

Motivated by an example in nutritional epidemiology, we investigate some design and analysis aspects of linear measurement error models with missing surrogate data. The specific problem investigated consists of an initial large sample in which the response (a food frequency questionnaire, FFQ)...