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Comments on: Data science, big data and statistics

  • Ruey S. TsayEmail author
Discussion
  • 16 Downloads

I like to congratulate Professors Galeano and Pena for the stimulating and thought-provoking article on Data Science and Big Data. It is timely and insightful. There is no doubt that big data and machine learning will affect statistical science in many ways, ranging from teaching to reasoning to data analysis to research. We are lucky that we now have tremendous amount of data to analyze, moving statistical science a step closer to the real world. At the same time, we are humble that we know so little about what statistics can contribute in this ever-increasing information era. The authors selected seven areas to discuss where the availability of big and complex data has challenged the traditional statistical analysis and shaped recent statistical developments. While agreeing wholeheartedly with most of their arguments, I do see areas in which statistics can contribute more than what was stated in the paper. Statistics can also shape the way machine learners and big data scientists...

Mathematics Subject Classification

62-07 62P20 62P25 

Notes

References

  1. Han Y, Tsay RS (2020) High-dimensional linear regression for dependent data with applications to nowcasting. Statistica Sinica (to appear)Google Scholar
  2. Han Y, Tsay RS, Wu WB (2018) Robust estimation of high-dimensional generalized linear models for dynamically dependent data (Preprint)Google Scholar
  3. Johnson JH, Gluck M (2016) Everydata’: the misinformation hidden in the litter data you consume every day. Bibliomotion, New YorkCrossRefGoogle Scholar
  4. Tsay RS (2014) Multivariate time series analysis with r and financial applications. Wiley, HobokenzbMATHGoogle Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.Booth School of BusinessUniversity of ChicagoChicagoUSA

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