|
Abstract:
|
Problems involving classification of high-dimensional data, and `highly multiple' hypothesis testing, arise frequently in the analysis of genetic data and complex signals. Their theoretical elucidation raises challenges, however. We address this by interpreting small samples of high-dimensional data. Depending to some extent on how erratic the time-series are, important features of classifiers, or of multiple hypothesis testing procedures, can be accessed by exploring properties of time-series models. For example, it can be shown that, in the context of multiple hypothesis testing, the assumption of independence is much less of an issue in high-dimensional settings than in conventional, low-dimensional ones. Similar arguments can be employed to explore other aspects of the analysis of high-dimensional data.
|
