In this course, the main characteristics of functional and shape data analysis are introduced. After the course, the student is able to apply appropriate descriptive and smoothing methods on functional and shape data sets. The student learns to apply linear and mixed models to functional and shape data sets, and acquires understanding how to use derivatives in functional data analysis and how to do Procrustes analysis in statistical shape analysis. Also, main shape distributions are introduced to the student.

Contents

- Introduction to functional and shape data analysis with R and Matlab

- Functional principal component and canonical correlation analysis

- Linear and mixed models for functional and shape data sets

- Linear differential operators in functional data analysis

- Procrustes analysis in statistical shape analysis

- Shape distributions

TTY:n opiskelijat ilmoittautuvat kurssille ristiinopiskelupalvelun ohjeiden mukaisesti.

Recommended preceding studies:
Understanding of basics of statistics and probability. For example minimum of UTA courses consists of MTTTP1 Introduction to Statistics and MTTTP5 Basics of Statistical Inference (or equivalent).