final_program_abstracts[1]

11 IMSC Session Program
A functional relationship model for simultaneous data series
Monday - Poster Session 4
Xiaoshu Lu
Finnish Institute of Occupational Health, Helsinki, Finland
Modelling the relationship between simultaneous data series is important for a wide
variety of applications. Despite apparently wide application, methodologies are at
present still inadequate. The well-known modelling technique is longitudinal data
analysis. Longitudinal data analysis mainly focuses on how to handle the withinperson
correlations among the repeated measurements. Data are often obtained with
few measures per subject and the models are formulated as linear. For two
longitudinal data with large-scale measures for subjects, the dimensionality is high,
hence there are few robust alternatives that can successfully address the unique
features characterised by the data. As such, another technique, referred to as
functional data analysis, is often employed. Various smoothing techniques are
introduced and applied for analysing functional data sets, such as curves and shapes.
A sufficiently large amount of data is needed to adequately approximate the function.
However, many data series are short, hence functional data model may not be able to
simulate with reasonable accuracy. In addition, a significant characteristic of real life's
data is their nonlinear nature. It is thus desirable to devise a method able to discover
and identify the nonlinear structure of the relationship between the data series. The
purpose of this study is to present a new mathematical methodology for addressing all
these issues. We extend the literature to both periodic time series and longitudinal
data. The main difference of the proposed model from other methods is its capability
for identifying complex nonlinear structure of the relationship behind the
simultaneous data series. We use singular value decomposition technique to extract
and model the dominant relationship between two data series. The functional
relationship can be used to explore complex interplay among the mechanical and
physical factors which govern the targeting system. The dataset of measured
computer-related workload and health outcome was used to test the proposed model
with promising results even though the data suffer from a number of limitations such
as collection of time series of the data is short. In addition, computation algorithms
are relatively simple which are easily computed by computers with available
commercial software.
Abstracts 45