ISBIS - Statistical Frontiers in Functional Data: A Spotlight on ANOVA Techniques

Abstract: As the number of observation points examined in studies increases, it is assumed that these points come from an underlying real function. Statistical methods that have been developed for this type of data are called functional data analysis. Therefore, there have been a great interest to adapt some traditional statistical methods into the functional data sets like hypothesis testing, regression or analysis of variance. n this study, the significance and key features of Functional Data Analysis are thoroughly examined, with particular emphasis on the application of ANOVA methods. The analysis delves into how Functional Data Analysis provides a robust framework for handling complex, multidimensional data that vary over continuous domains, such as time or space. Special attention is given to how ANOVA approaches can be adapted and applied in this context to assess differences between groups or treatments across functional observations. Furthermore, the study highlights the advantages of using these techniques to uncover deeper insights from data, offering a comprehensive perspective on their practical implications in various fields of research.