In this project, we aim to develop new models and statistical methods for the analysis of data along a continuum.
Data collected along a continuum, such as three-dimensional images or temporal weather data, are increasingly common in fields like medical imaging, psychology, and climate research. Functional data analysis approaches have been developed to handle such datasets, but their application remains limited.
For instance, estimation methods for studies with missing data are still underdeveloped, and applying these approaches to high-dimensional data poses challenges due to the need for calculating multi-dimensional covariance matrices.
In this project, we aim to develop the necessary models and estimation methods to address these issues.
