Functional data analysis (FDA) is a statistical framework that allows for the analysis of
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …

In this study, we investigate how Wilks' lambda, Pillai's trace, Hotelling's trace, and Roy's
largest root test statistics can be affected when the normal and homogeneous variance …

Functional data, ie, observations represented by curves or functions, frequently arise in
various fields. The theory and practice of statistical methods for such data is referred to as …

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown
strategy to contrast the spread of virus. Many studies have already documented that the …

The methodological contribution in this paper is motivated by biomechanical studies where
data characterizing human movement are waveform curves representing joint measures …

Multivariate functional data are frequently obtained in many scientific or industrial areas
where several functions for a statistical unit are observed over time. It is often interesting to …

Multivariate functional data are prevalent in various fields such as biology, climatology, and
finance. Motivated by the World Health Data applications, in this study, we propose and …

For longitudinal studies with multivariate observations, we propose statistical methods to
identify clusters of archetypal subjects by using techniques from functional data analysis and …

Understanding drivers' reactions to in-vehicle forward collision warnings (FCWs) is vital for
advancing FCW design and improving road safety. However, past studies often used …

OCCUPATIONAL APPLICATIONS Musculoskeletal disorders are prevalent among
warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it …