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Rejoinder on: Data science, big data and statistics

  • Pedro GaleanoEmail author
  • Daniel Peña
Discussion
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We are very grateful to all the discussants for agreeing to comment on our paper, for their encouraging words about our work and for their wise insights and ideas provided in their contributions. Their comments have broadened the scope of our presentation and have enriched the relationships among data science, big data and statistics with many useful references.

We fully agree on the importance of image data in health applications emphasized by Prof. Bühlmann, and we expect more use of image data in all fields of science. Prof. Bühlmann also stressed an important point that we do not cover in our article: stability analysis of the model, or prediction rule, fitted to the data. With this objective, we can estimate the model in different subsamples, that in particular could be different clusters, or create new data sets by perturbation of the original sample with some distribution. This is an important area of research and is connected to the comments of Prof. Delicado, on model...

Mathematics Subject Classification

62A01 62H99 

Notes

References

  1. Abdulah S, Ltaief H, Sun Y, Genton MG, Keyes DE (2018) Exageostat: a high performance unified software for geostatistics on manycore systems. IEEE Trans Parallel Distrib Syst 29:2771–2784CrossRefGoogle Scholar
  2. Alonso AM, Galeano P, Peña D (2019) A robust procedure to build dynamic factor models with cluster structure (manuscript submitted) Google Scholar
  3. Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Quar 20:451–468CrossRefGoogle Scholar
  4. Bhattacharyya S, Bickel PJ (2015) Subsampling bootstrap of count features of networks. Ann Stat 43(6):2384–2411MathSciNetCrossRefzbMATHGoogle Scholar
  5. Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2011) Introduction to meta-analysis. Wiley, New YorkzbMATHGoogle Scholar
  6. Breiman L (2001a) Random forests. Mach Learn 45:5–32CrossRefzbMATHGoogle Scholar
  7. Breiman L (2001b) Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci 16:199–231MathSciNetCrossRefzbMATHGoogle Scholar
  8. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Contemp Clin Trials 7(3):177–188CrossRefGoogle Scholar
  9. Draper D (1995) Assessment and propagation of model uncertainty. J R Stat Soc B 57:45–97MathSciNetzbMATHGoogle Scholar
  10. Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999) Bayesian model averaging: a tutorial. Stat Sci 14:382–401MathSciNetCrossRefzbMATHGoogle Scholar
  11. Leamer EE (1978) Specification searches. Wiley, New YorkzbMATHGoogle Scholar
  12. Ley C, Verdebout T (2017) Modern directional statistics. CRC Press, Boca RatonCrossRefzbMATHGoogle Scholar
  13. Peña D, Rodríguez J, Tiao GC (2003) Identifying mixtures of regression equations by the sar procedure. In: Bernardo JM, Bayarri MJ, Berger JO, Dawid AP, Herckerman D, Smith AFM (eds) Bayesian Statistics, vol 7. Oxford University Press, Oxford, pp 327–347Google Scholar
  14. Peña D, Tsay RS, Zamar R (2019) Empirical dynamic quantiles for visualization of high-dimensional time series. Technometrics.  https://doi.org/10.1080/00401706.2019.1575285
  15. Spodarev E (2017) Stochastic geometry, spatial statistics and random fields. Springer, New YorkGoogle Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.Departamento de Estadística and Institute of Financial Big DataUniversidad Carlos III de MadridGetafeSpain

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