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, Volume 28, Issue 2, pp 353–356 | Cite as

Comments on: Data science, big data and statistics

  • Jian Qing ShiEmail author
  • Shane Halloran
Discussion
  • 161 Downloads

We are delighted that Galeano and Peña give an insightful discussion on the recent change and development of statistical methods driven by big data. Here, we will introduce a different type of example, showing briefly how to combine traditional statistical techniques with ‘machine learning’ models. (Indeed, the latter could be named as ‘modern’ statistical techniques.) This links to the methods discussed in Sections 2.1, 2.2, 2.4, 2.5 and 2.6 in their paper.

The aim of the example is to model the recovery level of stroke rehabilitation patients remotely using accelerometer smartwatch movement data (see, e.g. Noorkõiv et al. 2014). Accelerometer sensors are ideal for this application, because they are cheap and unobtrusive and allow for data collection in naturalistic conditions wherever the user is situated. Since the data are collected over a longer period of time compared to laboratory-based assessments, the data are less likely to exhibit a high degree of observation bias. In...

Notes

References

  1. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, vol 1. Springer, BerlinzbMATHGoogle Scholar
  2. Noorkõiv M, Rodgers H, Price CI (2014) Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies. J Neuroeng Rehabil 11(1):144CrossRefGoogle Scholar
  3. Shi JQ, Choi T (2011) Gaussian process regression analysis for functional data. CRC Press, Boca RatonCrossRefzbMATHGoogle Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.School of Mathematics, Statistics and PhysicsNewcastle UniversityNewcastleUK

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