This page provides an overview, alphabetically by author(s), of all articles on big data methodology, IT or quality useful for WP8 Methodology. See here for documentation on big data in general and on the other ESSnet Big Data workpackages.

• L. Altin, M. Tiru, E. Saluveer & A. Puura (2015): Using Passive Mobile Positioning Data in Tourism and Population Statistics, NTTS 2015 Conference abstract
• A. Arai, Z. Fan, D. Matekenya & R. Shibasaki (2016): Comparative Perspective of Human Behavior Patterns to Uncover Ownership Bias among Mobile Phone Users
• AAPOR (2013): Report of the Task Force on Non-probability sampling, June.
• AAPOR (2015): American Association for Opinion Research Report on Big Data
• R.L. Ackoff (1989): From Data to Wisdom, Journal of Applied Systems Analysis 16, 3-9
• R. Agrawal & R. Srikant (1994): Fast algorithms for mining association rules in large databases, Proceedings of the 20th International Conference on Very Large Databases, 487-499, Santiago, Chile
• G.M. Amdahl  (1967): Validity of the single processor approach to achieving large scale computing capabilities, AFIPS Conference Proceedings 30, 483-485
• ASA-working group (2014): Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society, report of a Working Group of the
• M. Assay (2012): Big Data is now TOO BIG - and we're drowning in toxic information, Just why are we hoarding every last binary bit?, The Register, Cloud Business, 4 June
• J.W. Ayers, B.M. Althouse, J.P. Allem, et al. (2013): Seasonality in seeking mental health information on Google, American Journal of Preventive Medicine 44, 520-525
• J.W. Ayers, K. Ribisl & J.S. Brownstein (2011): Using Search Query Surveillance to Monitor Tax Avoidance and Smoking Cessation following the United States' 2009 “SCHIP” Cigarette Tax Increase, PLoS ONE 6(3): e16777
• D. Ayoubkhani (2012): An investigation into using Google Trends as an administrative data source in ONS, Seminar on New Frontiers for Statistical Data Collection, UNECE Conference of European Statisticians, Geneva
• F. Bacchini, M. Dalo, S. Falorsi, et al. (2014): Does Google index improve the forecast of Italian labour market?, Proceedings of the 47th Scientific Meeting of the Italian Statistical Society, Cagliari
• J. Bai, J. Fan, R. Tsay (2016): Special Issue on Big Data, Journal of Business and Economic Statistics 34(4), 487-488
• R. Baker, J.M. Brick, N.A. Bates, M. Battaglia, M.P. Couper, J.A.  Dever, K.J. Gile, R. Tourangeau (2013): Report on the AAPOR Task Force on Non-Probability Sampling. AAPOR report, May
• C. Bange, T. Grosser & N. Janoschek (2015): Big data use cases 2015: Getting real on data monetization, resreport, BARC Research
• G. Bello-Orgaz, J.J. Jung & D. Camacho (2016): Social big data: Recent achievements and new challenges, Information Fusion 28, 45–59
• M. Beresewicz (2016): Internet data sources for real estate market analysis. PhD Dissertation.
• J. Bethlehem (2010): Selection bias in web surveys. International Statistical Review, 78(2), 16–188, Wiley Online Library
• J. Bethlehem (2010): Statistics without surveys? About the past, present and future of data collection in the Netherlands, Presentation for the 2010 International Methodology Symposium of Statistics Canada, October 26-29, Ottawa, Canada
• J. Bethlehem & S. Biffignandi (2012): Handbook of web surveys. John Wiley and Sons
• M.A. Beyer & L. Douglas (2012): it-glossary/big-data/ The Importance of Big Data: A Definition. Gartner report, June version, ID Number: G00235055.
• P.J. Bickel, C. Chen, J. Kwon, J. Rice, E. van Zwet & P. Varaiya (2007): Measuring Traffic. Statistical Science, 22(4), 581–597
• P. Biemer (2014): Total Survey Error: Adapting the Paradigm for Big Data
• V.D. Blondel, A. Decuyper & G. Krings (2015): A survey of results on mobile phone datasets analysis. EPJ Data Science, 4(1), 1. Springer Berlin Heidelberg
• J. Bollen, H. Mao & X-J. Zeng (2011): Twitter mood predicts the stock market, Journal of Computational Science 2(1), 1-8
• D. Bollier (2010): The Promise and Peril of Big Data. Washington, DC: Aspen Institute, Communications and Society Program
• A. Börsch-Supan, D. Elsner, H. Fassbender, R. Kiefer, D. McFadden & J. Winter (2004): How to make internet surveys representative: A case study of a two-step weighting procedure
• O. ten Bosch & D. Windmeijer (2014): On the use of internet robots for official statistics, UNECE meeting on the Management of Statistical Information Systems (MSIS) Dublin, Ireland
• D.M. Boyd & N.B. Ellision (2007): Social Network Sites: Definition, History, and Scholarship, Journal of Computer-Mediated Communication 13(1), 210–230
• B. Braaksma, P. Daas, M. Offermans, M. Puts, M. Tennekes (2014): Big Data and official statistics: local experiences and international initiatives. Paper for the 47th Scientific Meeting of the Italian Statistical Society, 11-13 June, Cagliari, Italy
• M. Braun (2015): Three Things About Data Science You Won't Find In the Books. Weblog 5th April.
• L. Breiman (2001): Statistical Modeling: The Two Cultures. Statistical Science 16(3), 199-231
• L. Breiman, J. Friedman, C.J. Stone & R.A. Olshe  (1984): Classification and Regression Trees. CRC Press
• J.M. Brick (2013): Unit Nonresponse and Weighting Adjustments : A Critical Review. Journal of Official statistics 29(3), 329–353
• D.J. Buckeley  (1968): A Semi-Poisson Model of Traffic Flow, Trans. Sci. 2, 107-133
• B. Buelens, H.J. Boonstra, J. Van den Brakel & 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
• B. Buelens, J. Burger & J. Van den Brakel (2015): Predictive inference for non-probability samples: a simulation study. Discussion paper 2015, Statistics Netherlands, The Hague/Heerlen, The Netherlands
• B. Buelens, P. Daas, J. Burger, M. Puts & J. Van den Brakel (2014): Selectivity of Big Data, Discussion Paper 201411, Statistics Netherlands, The Hague/Heerlen, The Netherlands
• B. Buelens, P. Daas, J. Van den Brakel (2012): Data Mining for Official Statistics: Challenges and Opportunities. Paper 915 of 12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, Brussels, Belgium
• E. Cambria & B. White (2014): Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine 9(2), 48–57
• L.J. Carr LJ & S.I. Dunsiger (2012): Search Query Data to Monitor Interest in Behavior Change: Application for Public Health, PLoS ONE 7(10), e48158, doi:10.1371/journal.pone.0048158
• N. Carr (2010): The shallow, what Internet is doing to our brain, W.W, Norton and Company, New York
• A. Cavallo & R. Rigobon (2016): The Billion Prices Project: Using Online Prices for Measurement and Research, National Bureau of Economic Research Working Paper No. 22111
• R. Chambers (2009): Regression Analysis of Probability-Linked Data, Official statistics research series. Wellington: Statistics New Zealand (PDF)
• R. Chambers & H. Chandra (2013): A random effect block bootstrap for clustered data. Journal of Computational and Graphical Statistics 22(2), 452–470
• R. Chambers & R. Clark (2012): An introduction to model-based survey sampling with applications, (Vol. 37) OUP Oxford
• R. Chambers & N. Tzavidis (2006): M -quantile models for small area estimation, Biometrica 93(2), 255–268
• M. Chen, S. Mao & Y. Liu (2014): Big data: A survey, Mobile Networks and Applications 19(2), 171–209
• P. Cheung  (2012): Big Data, Official Statistics and Social Science Research: Emerging Data Challenges. Presentation at the December 19th World Bank meeting, Washington.
• H. Choi &  H. Varian (2011): Predicting the present with Google Trends, Technical Report
• R.M. Cormack (1989): Log-linear models for capture-recapture, Biometrics, 395–413
• M. Couper (2013): Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys. Survey Research Methods 7(3), 145-156
• J.W. Crampton, M. Graham, A. Poorthuis, T. Shelton, M. Stephens, M.W. Wilson & M. Zook (2013): Beyond the geotag: situating big data and leveraging the potential of the geoweb, Cartography and Geographic Information Science 40(2), 130-139
• P.J.H. Daas & MJ Puts (2014): Big data as a source of statistical information. The Survey Statistician 69, 22-31
• P. Daas (2012): Big Data and official statistics. Sharing Advisory Board, Software Sharing Newsletter 7, 2-3
• P. Daas & J. Burger (2015): Profiling Big Data sources to assess their selectivity. Abstract for the New Techniques and Technologies for Statistics 2015 conference, Brussels, Belgium
• P. Daas, S. De Broe & M. van Meeteren (2017): Center for Big Data Statistics at Statistics Netherlands. Abstract for the New Techniques and Technologies for Statistics 2017 conference, Brussels, Belgium
• P. Daas, M. Puts & R. Renssen (2017): On Big Data based Statistical Inference. Abstract and poster for the 3rd UCL Workshop on the Theory of Big Data, June 26th-28th, London, UK
• P.J.H. Daas (2013): Big Data and official statistics. The relevance of many tweets (in Dutch) STAtOR 14(3-4), 21-23
• P.J.H. Daas & M.J.H. Puts (2014): Social Media Sentiment and Consumer Confidence. European Central Bank Statistics Paper Series No. 5, Frankfurt, Germany
• P.J.H. Daas & M.P.J. Van der Loo (2013): Big Data (and official statistics), paper presented at the 2013 Meeting on the Management of Statistical Information Systems, Paris–Bangkok, France-Thailand.
• P.J.H. Daas, B. Braaksma, R. Aly, Y. Engelhardt, D. Hiemstra &  R. Zurita Milla (2016): Big Data Masterclass and DataCamp 2015. Discussion paper 201615, Statistics Netherlands, The Hague/Heerlen, The Netherlands
• P.J.H. Daas & B. Buelens (2017): Big data, bias and ways to correct for it. Abstract for the Big Data and ethics session at the 61st World Statistics Congress (ISI 2017) July 16th-21st, Marrakech, Morocco
• P.J.H. Daas, J. Burger, L. Quan, O. ten Bosch & M. Puts (2016): Profiling of Twitter Users: a big data selectivity study. Discussion paper 201606, Statistics Netherlands, The Hague/Heerlen, The Netherlands
• P.J.H. Daas, M.J.H. Puts, B. Buelens & P.A.M. van den Hurk (2015): Big data as a source for official statistics. Journal of Official Statistics 31, 249–269
• P.J.H. Daas, M. Puts, M. Tennekes &  A. Priem (2014): Big Data as a Data Source for Official Statistics: experiences at Statistics Netherlands. Proceedings of Statistics Canada International Methodology Symposium 2014, Gatineau, Canada
• P.J.H. Daas & M.J.H. Puts (2014): Sentiment analysis of Mexican tweets: smileys and emoticons. A Big Data sandbox studies for the social data task team of the UNECE taskforce, UNECE.
• P.J.H. Daas, M. Roos, C. de Blois, R. Hoekstra, O. ten Bosch & Y. Ma (2011): New data sources for statistics: Experiences at Statistics Netherlands. Discussion paper 201109, Statistics Netherlands, The Hague/Heerlen, The Netherlands
• P.J.H. Daas, M. Roos, M. Van de Ven & J. Neroni (2012): Twitter as a potential data source for statistics, Discussion Paper 201221, Statistics Netherlands, The Hague/Heerlen, The Netherlands
• E. De Jonge, M. van Pelt & M. Roos (2012): Time patterns, geospatial clustering and mobility statistics based on mobile phone network data. Discussion paper 201214, Statistics Netherlands
• F. De Meersman, G. Seynaeve, M. Debusschere,  P. Lusyne, P. Dewitte, Y. Baeyens, A. Wirthmann, C. Demunter, F. Reis & H.I. Reuter (2016): Assessing the Quality of Mobile Phone Data as a Source of Statistics. Presentation for the European Conference on Quality in Official Statistics 2016, Madrid, Spain
• T. De Waal, M. Puts & P. Daas (2014): Statistical Data Editing of Big Data. Paper for the Royal Statistical Society 2014 International Conference, Sheffield, UK
• E. Demidenko (2004): Mixed Models. Theory and Applications. New York: Wiley
• C. Demunter & G. Seynaeve (2017): Better quality of mobile phone data based statistics through the use of signalling information – the case of tourism statistics, NTTS Conference, 13-17 March 2017 (paper and presentation download page)
• J.A. Dever & R. Valliant (2006): A Comparison of Model-Based and Model-Assisted Estimators under Ignorable and Non-Ignorable Nonresponse. Proceedings of the Section on Survey Research Methods, Washington DC: American Statistical Association, 2938–2945
• J. Deville & P. Lavallée (2006): Indirect sampling: The foundations of the generalized weight share method. Survey Methodology 32(2), 165—177
• J.-C. Deville & C.E. Särndal (1992): Calibration estimators in survey sampling. Journal of the American statistical Association 87(418), 376–382
• P. Deville, C. Linarde, S. Martine, M. Gilbert, F.R. Stevens, A.E. Gaughan, V.D. Blondela & A.J. Tatem (2014): Dynamic population mapping using mobile phone data, PNAS 111(45), 15888-15893
• L. Di Consiglio & T. Tuoto (2017): Small area estimation in the presence of linkage error
• Dialogic, Ministry of Economic affairs, Utrecht University (2008): Go with the dataflow! Analysing the Internet as a data source. Report for the Ministry of Economic affairs, version May 13th
• P.J. Diggle, K.-Y. Liang & S.L. Zeger (1994): Analysis of Longitudinal Data. Oxford: Oxford University Press
• L. Douglas (2012): The Importance of 'Big Data': A Definition. Gartner. Retrieved 21 June 2012.
• Economist (2010): Data, data everywhere! Special report of the Economist, February 27
• B. Efron  (2010): Large-scale inference: empirical Bayes methods for estimation, testing, and prediction. Institute of mathematical statistics monographs 1. Cambridge; New York: Cambridge University Press
• B. Efron & R. Tibshirani  (1986): Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science 1(1), 54–75
• B. Efron & T. Hastie  (2016): Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press
• L. Einav & J. Levin  (2014): Economics in the age of big data. Science 346(6210), 715-721, DOI: 10.1126/science.1243089
• C.K. Enders  (2010): Applied missing data analysis. Guilford Press
• European Commission (2014): Feasibility Study on the Use of Mobile Positioning Data for Tourism Statistics, Eurostat
• European Statistical System Committee (2013): Scheveningen Memorandum on Big Data and Official Statistics
• European Statistical System Committee (2014): Big Data Action Plan and Roadmap
• D. Evans & S. Bratton  (2012): Social Media Marketing: An Hour a Day. Sybex/Wiley and Sons 2nd edition
• G. Eysenbach (2009): Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of Medical Internet Research 11(1)
• E. Fabrizi, N. Salvati,  M. Pratesi & N. Tzavidis (2014): Outlier robust model-assisted small area estimation. Biometrical Journal 56(1), 157–175
• J. Fan, F. Han & H. Liu (2014): Challenges of Big data analysis. National Science Review 1(2), 293-314
• R.E. Fay  (1996): Alternative paradigms for the analysis of imputed survey data. Journal of the American Statistical Association 91(434), 490–498
• M. Feder & D. Pfeffermann  (2015): Statistical inference under non-ignorable sampling and non-response. University of Southampton.
• S.E. Fienberg  (1972): The multiple recapture census for closed populations and incomplete 2k contingency tables. Biometrika 59(3), 591–603
• P. Flach (2014): Machine Learning, the Art and Science of Algorithms that Make Sense of Data, 4th edition. Cambridge University Press, Cambridge, UK
• L. Flekova & 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
• J. Fosen & L.-C. Zhang  (2011): The approach to quality evaluation of the micro-integrated employment statistics
• J. Friedman, T. Hastie & R. Tibshirani, (2001): The elements of statistical learning (Vol. 1) Springer series in statistics Springer, Berlin
• B. Fry (2008): Visualizing Data: Exploring and Explaining Data with the Processing Environment. Sebastopol, CA: OReilly Media Inc.
• A. Fyhrlund, B. Fridlund & B. Sundgren  (2005): Using Text Mining in Official Statistics, Knowledge Mining, Proceedings of the NEMIS 2004 Final Conference, Studies in Fuzziness and Soft Computing 185, 201-211
• A. Gandomi & M. Haider (2015): Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35(2), 137–144
• A. Gelman & J. Hill (2009): Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press
• A. Gelman  (2007): Struggles with Survey Weighting and Regression Modelling. Statistical Science 22(2), 153–164.
• A. Ghazal, T. Rabl, M. Hu, F. Raab, M. Poess, A. Crolotte & H.-A. Jacobsen (2013): Big-Bench: Towards an industry standard benchmark for big data analytics. In Proceedings of the 2013 international conference on Management of data - SIGMOD '13. Association for Computing Machinery (ACM)
• J.D Gibons & S. Chakraborit  (2003): Nonparametric Statistical Inference, 4th Ed. CRC Press, New York, USA
• J. Ginsberg,  M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski & L. Brilliant: Detecting influenza epidemics using search engine query data. Nature 457(7232): 1012–1014. doi:10.1038/nature07634
• M. Glasson, J. Trepanier, V. Patruno, P. Daas, M. Skaliotis & 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.
• S.A. Golder & M.W. Macy (2011): Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 1878-1881
• V. van Grinsven & G. Snijkers (2015): Sentiments and Perceptions of Business Respondents on Social Media: an Exploratory Analysis. Journal of Official Statistics 31, 283–304
• P. Groves, B. Kayyali, D. Knott & S.V. Kuiken (2013): The big data revolution in healthcare: Accelerating value and innovation. resreport, McKinsey and Company, Center for US Health System Reform; Business Technology Office
• R.M. Groves (2011): Three Eras of Survey Research, Public Opinion Quarterly 75(5), 861-871
• I. Guyon & A. Elisseeff (2003): An Introduction to Variable and Feature Selection. JMLR special issue on variable and feature selection 3, 1157—1182
• G. Hager & G. Wellein (2010): Introduction to High Performance Computing for Scientists and Engineers, Boca Raton: Chapman and Hall/CRC Computational Science
• M. Hahsler, B. Grun, K. Hornik & C. Buchta (2010): Introduction to arules – A computational environment for mining association rules and frequent item sets
• A. Hajjem, F. Bellavance & D. Larocque  (2011): Mixed effects regression trees for clustered data. Statistics and Probability Letters 81(4), 451–459. Elsevier B.V
• A. Hajjem, F. Bellavance & D. Larocque  (2014): Mixed-effects random forest for clustered data. Journal of Statistical Computation and Simulation 84(6), 1313–1328
• T. Harford (2014): Big Data: are we making a big mistake? Significance 11 (5) 14-19
• I.A.T. Hashem, I. Yaqoob, N.B. Anuar, S. Mokhtar, A. Gani & S.U. Khan (2015): The rise of big data on cloud computing: Review and open research issues. Information Systems 47, 98–115
• H. Hassani, G. Saporta & E. Sirimal Silvia (2014): Data Mining and Official Statistics: The Past, the Present and the Future. Big Data 2, 1–10.
• T. Hastie, R. Tibshirani & J. Friedman (2009): The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer Science þ Business Media, LLC
• J.J. Heckman (1976): The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5, 475–492
• N. Heerschap (2014): Mobile phone data and other new sources for tourism statistics (in Dutch) Section 10.2, Statistics Netherlands book on Tourism, 158-168, The Hague, The Netherlands
• N.M. Heerschap, S.A. Ortega Azurduy, A.H. Priem & M.P.W. Offermans (2014): Innovation of tourism statistics through the use of new Big Data sources, paper presented at the Global Forum on Tourism Statistics, Prague.
• G.T. Heineman, G. Pollice & S. Selkow (2009): Algorithms in a Nutshell, a desktop quick reference. OReilly Meia Inc. Sebastopol, USA
• H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F.B. Cetin & S. Babu (2011): Starfish: A self-tuning system for big data analytics 49
• T. Hey, S. Tansley, K. Tolle  (2009): The Fourth Paradigm, Data-Intensive Scientific Discovery. Microsoft Research, Redmond, Washington, USA
• M. Hildebrandt & S. Gutwirth (2013): Profiling the European Citizen. Cross Disciplinary Perspectives. Springer, Dordrecht, the Netherlands
• E. Hoogteijling (2016): Modernisation of price collection at Statistics Netherlands. Presentation at the ESS Modernisation Workshop, 16–17 March, Bucharest
• M. Houbiers (2004): Towards a Social Statistical Database and Unified Estimates at Statistics Netherlands. Journal of Official Statistics 20(1), 55–75
• M. Houbiers, P. Knottnerus, A.H. Kroese, R.H. Renssen & V. Snijders (2003): Estimating consistent table sets: position paper on repeated weighting. Statistics Netherlands, Discussion paper 3005, 2003
• H. Hu, Y. Wen, T.-S. Chua & X. Li (2014): Toward scalable systems for big data analytics: A technology tutorial. IEEE Access 2, 652–687
• Y. Hu, J. Fowler Wood, V. Smith & N. Westbrook (2004): Friendships through OM: Examining the relationship between Instant Messaging and Intimacy, Journal of Computer-mediated Communications 10(1)
• Hulliger, Beat, R. Lehtonen, R. Münnich, P. Jacques, European Commission & Eurostat (2012): Analysis of the Future Research Needs for Official Statistics. Luxembourg: Publications Office
• Internet statistics guide (2002): Complete Guide to Internet Statistics and Research
• M. Ito & all (2010): Hanging out, Messing around and Geeking Out: Kids living and learning with new media
• B. Janssen (2010): Web data collection for household surveys at Statistics Netherlands. Internal Report CBS
• A. Java, X. Song, T. Finin, F. Tseng (2007): Why we twitter: understanding microblogging usage and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, ACM New York, USA
• J. Jonas  (2012): Interview: Data protection challenge of the future: Big Data. Data Protection Law and Policy Newsletter 9(7)
• S. Jones (1998): Doing Internet Research: Critical Issues and Methods for Examining the Net. Sage Publications, Inc. California, USA
• K.D. Bell (2011): Comparing methods for estimation of daytime population in Downtown Indianapolis, Indiana, Master of Science thesis, Dept. Geography, Indiana University
• C. Kadushin (2012): Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press, New York, USA
• A. M. Kaplan & M. Haenlein (2010): Users of the world, unite! The challenges and opportunities of social media, Business Horizons 53(1), 59-68
• G. Kim & R. Chambers (2012): Regression analysis under incomplete linkage. Statistica Neerlandica, 56(9), 2756–2770
• R. Kitchin (2013): Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography 3(3), 262–267
• R. Kitchin (2014): Big data, new epistemologies and paradigm shifts. Big Data and Society 1(1) 1–12
• R. Kitchin (2015): The opportunities, challenges and risks of big data for official statistics. Statistical Journal of the IAOS 31(3), 471-481, DOI: 10.3233/SJI-150906
• R. Kitchin & G. McArdle (2016): What makes big data, big data? exploring the ontological characteristics of 26 datasets. Big Data and Society 3(1), 1–10
• Knight & Burn (2005): Developing a framework for assessing information quality on the World Wide Web, Informing Science J. 8, 159-172
• A.D.I. Kramer, J.E. Guillory & J.T. Hancock (2014): Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111(24), 8788-8790
• T. Kraska (2013): Finding the needle in the big data systems haystack. IEEE Internet Computing 17(1), 84–86
• H. Kwak, C. Lee, H. Park & S. Moon (2010): What is Twitter, a Social Network or a News Media? In: Proceedings of the 19th international conference on World wide web, ACM New York, NY, USA, 591-600
• P. Lahiri & M.D. Larsen (2005): Regression Analysis with Linked Data. Journal of the American Statistical Association 100(469), 222–230
• D. Laney (2013): 3D data management: Controlling data volume, velocity and variety. meta group. Application Delivery Strategies,(February 2001) (949)
• T. Lansdall-Welfare, V. Lampos & N. Cristianini (2012): Nowcasting the mood of the nation, Significance: Big Data special issue 9(4), 26-28
• P. Lavallée (2009): Indirect sampling (Vol. 7397) Springer Science and Business Media
• P. Lavallée (2015): Sample matching: Toward a probabilistic approach for web surveys and big data?
• D. Lazer, R. Kennedy, G. King & A. Vespignani (2014): The parable of Google Flu: traps in big data analysis. Science 343, 1203–1205
• D. Lazer, A. Pentland, L. Adamic, S. Aral, A.L. Barabási, D. Brewer & T. Jebara, Computational social science (2009): Science 323, 721
• S. Lee (2006): Propensity score adjustment as a weighting scheme for volunteer panel web surveys. Journal of Official Statistics 22(2), 329
• R. Lehtonen & A. Veijanen (2016): Estimation of poverty rate and quintile share ratio for domains and small areas In: Alleva G. and Giommi A. (eds.) Topics in Theoretical and Applied Statistics, New York: Springer, 153–165
• R. Lehtonen & A. Veijanen (2016): Model-assisted methods for small area estimation of poverty indicators. In: Pratesi M. (ed.) Analysis of Poverty Data by Small Area Estimation. Chichester: Wiley, 109–127
• R. Lehtonen & A. Veijanen (2012): Small area poverty estimation by model calibration. Journal of the Indian Society of Agricultural Statistics 66(1), 125–133
• J.M. Lepkowski, C. Tucker, J.M. Brick, E.D. De Leeuw, L. Japec, P.J. Lavrakas, M.W. Link & al. (Eds.): (2007) Advances in telephone survey methodology (Vol. 538) John Wiley and Sons
• J. Leskovec, A. Rajaraman & J.D. Ullman (2014): Mining of Massive Datasets, 2nd edition. Cambridge University Press, Cambridge, UK
• R. Little (2012): Calibrated Bayes: an Alternative Inferential Paradigm for Official Statistics (with discussion and rejoinder) Journal of Official Statistics 28(3), 309–372
• R.J. Little (2015): Calibrated bayes, an inferential paradigm for official statistics in the era of big data. Statistical Journal of the IAOS 31(4), 555–563. IOS Press
• A. Llorente, M. Garcia-Herranz, M. Cebrian & E. Moro (2015): Social Media Fingerprints of Unemployment. PloS ONE 10(5), e0128692. doi:10.1371/journal.pone.0128692
• W.-Y. Loh (2014): Fifty years of classification and regression trees. International Statistical Review 82(3), 329–348. Wiley Online Library
• S. Lohr (2009): Sampling: Design and analysis. Cengage Learning
• S. Lohr & J. Brick (2012): Blending domain estimates from two victimization surveys with possible bias. Canadian Journal of Statistics 40(4), 679–696
• London Workshop (2014): Statistics and Science, report on the London Workshop on the Future of the Statistical Sciences
• S. Lundström & C.-E. Särndal (1999): Calibration as a standard method for treatment of nonresponse. Journal of Official Statistics 15(2), 305–328
• C. Lynch  (2008): http:// dx.doi.org/10.1038/455028a Big Data: How Do Your Data Grow? Nature 455, 28–29
• Y. Ma  (2016): The Use of Advanced Transportation Monitoring Data for Official Statistics. Doctoral dissertation. Erasmus University, Rotterdam
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