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Statistical Inference, Learning and Models in Big Data

Author

Listed:
  • Beate Franke
  • Jean-FRANçois Plante
  • Ribana Roscher
  • En-shiun Annie Lee
  • Cathal Smyth
  • Armin Hatefi
  • Fuqi Chen
  • Einat Gil
  • Alexander Schwing
  • Alessandro Selvitella
  • Michael M. Hoffman
  • Roger Grosse
  • Dieter Hendricks
  • Nancy Reid

Abstract

No abstract is available for this item.

Suggested Citation

  • Beate Franke & Jean-FRANçois Plante & Ribana Roscher & En-shiun Annie Lee & Cathal Smyth & Armin Hatefi & Fuqi Chen & Einat Gil & Alexander Schwing & Alessandro Selvitella & Michael M. Hoffman & Roger, 2016. "Statistical Inference, Learning and Models in Big Data," International Statistical Review, International Statistical Institute, vol. 84(3), pages 371-389, December.
    • Handle: RePEc:bla:istatr:v:84:y:2016:i:3:p:371-389
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    File URL: http://hdl.handle.net/10.1111/insr.12176
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    References listed on IDEAS

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    1. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Ariel Kleiner & Ameet Talwalkar & Purnamrita Sarkar & Michael I. Jordan, 2014. "A scalable bootstrap for massive data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(4), pages 795-816, September.
    4. repec:bla:istatr:v:83:y:2015:i:3:p:436-448 is not listed on IDEAS
    5. P. Nguyen & P. E. Brown & J. Stafford, 2012. "Mapping Cancer Risk in Southwestern Ontario with Changing Census Boundaries," Biometrics, The International Biometric Society, vol. 68(4), pages 1228-1237, December.
    6. Mark Girolami & Ben Calderhead, 2011. "Riemann manifold Langevin and Hamiltonian Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 123-214, March.
    7. Wei Biao Wu, 2003. "Nonparametric estimation of large covariance matrices of longitudinal data," Biometrika, Biometrika Trust, vol. 90(4), pages 831-844, December.
    8. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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    Citations

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    Cited by:

    1. Yang Lu, 2025. "The Current Status and Developing Trends of Industry 4.0: a Review," Information Systems Frontiers, Springer, vol. 27(1), pages 215-234, February.
    2. Lu Han & Hanping Hou & Z. M. Bi & Jianliang Yang & Xiaoxiao Zheng, 2024. "Functional Requirements and Supply Chain Digitalization in Industry 4.0," Information Systems Frontiers, Springer, vol. 26(6), pages 2273-2285, December.
    3. Antoni Espasa & Guillermo Carlomagno, 2024. "Modelling high frequency non-financial big time series with an application to jobless claims in Chile," Working Papers Central Bank of Chile 1023, Central Bank of Chile.
    4. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.
    5. Espasa, Antoni & Carlomagno Real, Guillermo, 2023. "Tall big data time series of high frequency: stylized facts and econometric modelling," DES - Working Papers. Statistics and Econometrics. WS 37746, Universidad Carlos III de Madrid. Departamento de Estadística.

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