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Big Data as a Source for Official Statistics Cover

Big Data as a Source for Official Statistics

Journal of Official Statistics
Volume 31 (2015): Issue 2 (June 2015)
By: Piet J.H. Daas,  Marco J. Puts,  Bart Buelens and  Paul A.M. van den Hurk  
Open Access
|Jun 2015

References

  1. ASA. 2014. Discovery With Data: Leveraging Statistics with Computer Science to Transform Science and Society. July 2, 2014 version. Available at: http://www.amstat.org/policy/pdfs/BigDataStatisticsJune2014.pdf (accessed July 2014).
    Search in Google Scholar
  2. Beyer, M.A. and L. Douglas. 2012. The Importance of ‘Big Data’: A Definition. Gartner report, June version, ID Number: G00235055. Available at: http://www.gartner.com/it-glossary/big-data/ (accessed January 2013).
    Search in Google Scholar
  3. Breiman, L. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science 16: 99-231. Doi: http://dx.doi.org/10.1214/ss/1009213726.10.1214/ss/1009213726
    Search in Google Scholar
  4. Buelens, B., H.J. Boonstra, J. van den Brakel, and P. Daas. 2012. Shifting Paradigms in Official Statistics: from Design-Based to Model-Based to Algorithmic Inference. Discussion paper 201218, Statistics Netherlands, The Hague/Heerlen.
    Search in Google Scholar
  5. Buelens, B., P. Daas, J. Burger, M. Puts, and J. van den Brakel. 2014. Selectivity of Big Data. Discussion paper 201411, Statistics Netherlands, The Hague/Heerlen, The Netherlands.
    Search in Google Scholar
  6. Cheung, P. 2012. Big Data, Official Statistics and Social Science Research: Emerging Data Challenges. Presentation at the December 19th World Bank meeting, Washington.Available at: http://www.worldbank.org/wb/Big-data-pc-2012-12-12.pdf (accessed January 2013).
    Search in Google Scholar
  7. Coosto. 2013. Main page. Available at: http://www.coosto.com/uk/ (accessed August 2013).
    Search in Google Scholar
  8. Daas, P.J.H. and M.J.H. Puts. 2014. Social Media Sentiment and Consumer Confidence.Paper for the Workshop on using Big Data for Forecasting and Statistics, April 7-8, Frankfurt, Germany. Available at: https://www.ecb.europa.eu/pub/pdf/scpsps/ecbsp5.pdf (accessed April 2015).
    Search in Google Scholar
  9. Daas, P.J.H., M. Roos, M. van de Ven, and J. Neroni. 2012a. Twitter as a Potential Data Source for Statistics. Discussion paper 201221, The Hague/Heerlen: Statistics Netherlands.
    Search in Google Scholar
  10. Daas, P., M. Tennekes, E. de Jonge, A. Priem, B. Buelens, M. van Pelt, and P. van den Hurk. 2012b. Data Science and the Future of Statistics. Presentation at the first Data Science NL meetup, Utrecht University, Utrecht. Available at: http://www.slideshare.net/pietdaas/data-science-and-the-future-of-statistics (accessed December 2012).
    Search in Google Scholar
  11. De Jonge, E., M. van Pelt, and M. Roos. 2012. Time Patterns, Geospatial Clustering and Mobility Statistics Based on Mobile Phone Network Data. Discussion paper 201214, The Hague/Heerlen: Statistics Netherlands.
    Search in Google Scholar
  12. De Jonge, E., J. Wijffels, and J. van der Laan. 2014. “ffbase: Basic Statistical Functions for Package ff. R package version 0.11.3.” Available at: http://cran.r-project.org/web/packages/ffbase/index.html (accessed April 2015).
    Search in Google Scholar
  13. De Waal, T., J. Pannekoek, and S. Scholtus. 2011. Handbook of Statistical Editing and Imputation. Hoboken, NJ: John Wiley & Sons.10.1002/9780470904848
    Search in Google Scholar
  14. Engle, R.F. and C.W.J. Granger. 1987. “Co-Integration and Error Correction: Representation, Estimation, and Testing.” Econometrica 55: 251-276.10.2307/1913236
    Search in Google Scholar
  15. Eurostat. 2012. Internet Access and Use. Eurostat newsrelease 185/2012, December 18, 2012. Available at: http://epp.eurostat.ec.europa.eu/cache/ITY_PUBLIC/4-18122012-AP/EN/4-18122012-AP-EN.PDF (accessed January 2013).
    Search in Google Scholar
  16. Flekova, L. and I. Gurevych. 2013. Can We Hide in the Web? Large Scale Simultaneous Age and Gender Author Profiling in Social Media. Paper for the evaluation lab on uncovering plagiarism, authorship, and social software misuse at Conference and Labs Evaluation Forum 2013, September 23-26, Valencia, Spain.
    Search in Google Scholar
  17. Fry, B. 2008. Visualizing Data: Exploring and Explaining Data with the Processing Environment. Sebastopol, CA: O’Reilly Media Inc.
    Search in Google Scholar
  18. Glasson, M., J. Trepanier, V. Patruno, P. Daas, M. Skaliotis, and A. Khan. 2013. What does “Big Data” mean for Official Statistics? Paper for the High-Level Group for the Modernization of Statistical Production and Services, March 10.
    Search in Google Scholar
  19. Golder, S.A. and M.W. Macy. 2011. “Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures.” Science 30: 1878-1881. Doi: http://dx.doi.org/10.1126/science.1202775. 10.1126/science.1202775
    Search in Google Scholar
  20. Groves, R.M. 2011. “Three Eras of Survey Research.” Public Opinion Quarterly 75: 861-871. Doi: http://dx.doi.org/10.1093/poq/nfr057.10.1093/poq/nfr057
    Search in Google Scholar
  21. Hassani, H., G. Saporta, and E. Sirimal Silvia. 2014. “Data Mining and Official Statistics: The Past, the Present and the Future.” Big Data 2: 1-10. Doi: http://dx.doi.org/10.1089/big.2013.0038.10.1089/big.2013.0038
    Search in Google Scholar
  22. Hastie, T., R. Tibshirani, and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer Science þ Business Media, LLC.
    Search in Google Scholar
  23. Lansdall-Welfare, T., V. Lampos, and N. Cristianini. 2012. “Nowcasting the Mood of the Nation.” Significance 9: 26-28. Available at: http://www.significancemagazine.org/details/magazine/2468761/Nowcasting-the-mood-of-the-nation.html (accessed January 2013).10.1111/j.1740-9713.2012.00588.x
    Search in Google Scholar
  24. Lynch, C. 2008. “Big Data: How Do Your Data Grow?” Nature 455: 28-29. Doi: http:// dx.doi.org/10.1038/455028a.10.1038/455028a
    Search in Google Scholar
  25. Manton, J.H., V. Krishnamurthy, and R.J. Elliott. 1999. “Discrete Time Filters for Double Stochastic Poisson Processes and Other Exponential Noise Models.” International Journal of Adaptive Control and Signal Processing 13: 393-416.10.1002/(SICI)1099-1115(199908)13:5<393::AID-ACS561>3.0.CO;2-J
    Search in Google Scholar
  26. Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. Hung Byers. 2011. Big Data: The Next Frontier for Innovation, Competition, and Productivity. Report of the McKinsey Global Institute, McKinsey & Company.
    Search in Google Scholar
  27. NAS. 2013. Frontiers in Massive Data Analysis. Washington, DC: The National Academies Press.
    Search in Google Scholar
  28. NDW. 2012. The Database Explained. Brochure of the National Data Warehouse for Traffic Information, March. Available at: http://www.ndw.nu/download_files.php?action¼download_file&file_hash¼209140a807e959f06646b0311f79de26 (accessed December 2012).
    Search in Google Scholar
  29. O’Connor, B., R. Balasubramanyan, B.R. Routledge, and N.A. Smith. 2010. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. Carnegie Mellon University, Research Showcase. Available at: www.cs.cmu.edu/,nasmith/papers/oconnorþbalasubramanyanþroutledgeþsmith.icwsm10.pdf (accessed April 2015).
    Search in Google Scholar
  30. R Development Core Team. 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
    Search in Google Scholar
  31. Rajaraman, A. and J.D. Ullman. 2011. Mining of Massive Datasets. Cambridge: Cambridge University Press.10.1017/CBO9781139058452
    Search in Google Scholar
  32. Schutt, R. and C. O’Neil. 2013. Doing Data Science: Straight Talk from the Frontline.
    Search in Google Scholar
  33. Sebastopol, CA: O’Reilly Media. Scott, S.L., A.W. Blocker, F.V. Bonassi, H.A. Chipman, E.I. George, and R.E. McCulloch. 2013. Bayes and Big Data: The Consensus Monte Carlo Algorithm. Bayes 250. Available at: http://www.rob-mcculloch.org/some_papers_and_talks/papers/working/consensus-mc.pdf (accessed April 2015).
    Search in Google Scholar
  34. Statistics Netherlands. 2013. Consumer Confidence Survey. Available at: http://www.cbs.nl/en-GB/menu/methoden/dataverzameling/consumenten-conjunctuuronderzoek-cco.htm (accessed April 2013).
    Search in Google Scholar
  35. Struijs, P. and P.J.H. Daas. 2013. Big Data, Big Impact? Paper for the Seminar on Statistical Data Collection, September 25-27, Geneva. Switzerland
    Search in Google Scholar
  36. Tennekes, M., E. de Jonge, and P.J.H. Daas. 2013. “Visualizing and Inspecting Large Datasets with Tableplots.” Journal of Data Science 11: 43-58.10.6339/JDS.201301_11(1).0003
    Search in Google Scholar
  37. Van der Laan, J. 2013. LaF: Fast Access to Large ASCII files. R package version 0.5.
    Search in Google Scholar
  38. Zikopoulos, P., D. deRoos, K. Parasuraman, T. Deutsch, D. Corrigan, and J. Giles. 2012. Harness the Power of Big Data. New York: McGraw-Hill.
    Search in Google Scholar
DOI: https://doi.org/10.1515/jos-2015-0016 | Journal eISSN: 2001-7367
Journal RSS Feed
Language: English
Page range: 249 - 262
Submitted on: Aug 1, 2013
|
Accepted on: Sep 1, 2014
|
Published on: Jun 27, 2015
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Large data sets,
traffic data,
social media
Related subjects:
Mathematics,
Probability and statistics

© 2015 Piet J.H. Daas, Marco J. Puts, Bart Buelens, Paul A.M. van den Hurk, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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