The advance of modern sensor technologies enables collection multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this article, we present a non-parametric approach to predict the evolution for an in-service unit through borrowing strength other historical units. Our first decomposes each stream into linear combination eigenfunctions and thei...

The term “functional data” refers to data where each observation is a curve, a surface, or a hypersurface, as opposed to a point or a finite-dimensional vector. Functional data are intrinsically infinite dimensional and measurements on the same curve display high correlation, making assumptions of classical multivariate models invalid. An alternative approach, functional principal components an...

We introduced and validated an EEG data-based model of the sleep process with an arbitrary number of different sleep states and a high time resolution allowing modelling of sleep microstructure. The proposed probabilistic sleep model describes sleep via posterior probabilities of a finite number of microstates. Using the model, we extracted objective sleep parameters describing quantitative and...

Functional Data Analysis is a relatively new branch of Statistics devoted to describing and modelling data that are complete functions. Many relevant aspects of musical performance and perception can be understood and quantified as dynamic processes evolving as functions of time. In this paper, we show that Functional Data Analysis is a statistical methodology well-suited for research into the ...

detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (wsns). to address the problem of outlier detection in wireless sensor networks, in this paper we present a pca-based centralized approach and a dpca-based distributed energy-efficient approach for detecting outliers in sensed data in a wsn. the outliers in sensed data can be ca...