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Related Experiment Video

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Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
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Applications of functional data analysis: A systematic review.

Shahid Ullah1, Caroline F Finch

  • 1Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Faculty of Health Sciences, Flinders University, Adelaide, Australia. shahid.ullah@flinders.edu.au

BMC Medical Research Methodology
|March 21, 2013
PubMed
Summary
This summary is machine-generated.

Functional data analysis (FDA) offers powerful tools for time series data, with increasing applications in biomedicine. Despite its benefits, wider adoption is needed for better modeling and predictions, especially in public health.

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Area of Science:

  • Statistics
  • Data Science
  • Biostatistics

Background:

  • Functional data analysis (FDA) is a growing statistical method for analyzing time series data.
  • Key components include smoothing, data reduction, and modeling techniques.

Purpose of the Study:

  • To systematically review the applications of Functional Data Analysis (FDA) in peer-reviewed literature from 1995-2010.
  • To identify trends in FDA methodologies and application fields.

Main Methods:

  • A systematic review of 11 electronic databases was performed.
  • Included studies published between 1995 and 2010, excluding methodological papers and non-English articles.

Main Results:

  • 84 FDA application articles were identified, with a surge in publications since 2005.
  • Biomedicine was the most common application area (21.4%).
  • B-spline smoothing (29.8%) and functional principal component analysis (60.7%) were frequently used. Functional linear models were used in 25% of studies, and FDA for forecasting in only 8.3%.

Conclusions:

  • FDA provides significant benefits for time series analysis, particularly in public health and biomedical research.
  • Wider application of FDA can enhance modeling and prediction from correlated data.
  • FDA's ability to avoid a priori age and time assumptions is a key advantage.