List of Publications
Statistics Netherlands publication period (2020 - 2022)
Links to other publications 1, 2, 3, 4, 5, 6, 7.
Daas, P., Jansen, J. (2020) Model degradation in web derived text-based models. Paper for the 3rd International Conference on Advanced Research Methods and Analytics (CARMA), pp 77-84. DOI-link (paper).
Daas, P.J.H., van der Doef, S. (2020) Detecting Innovative Companies via their Website. Statistical Journal of IAOS 36(4), pp. 1239-1251. DOI-link (online link).
Daas, P.J.H. (2020) Developing a Corona sentiment indicator: Methodological justification (in Dutch). CBS-report. (report and online link).
Daas, P., Maslankowski, J., Salgado, D., Quaresma, S., Tuoto, T., Di Consiglio, L., Brancato, G., Righi, P., Six, M., Kowarik, A. (2020) First draft of the Methodological report. Deliverable K5, Workpackage K, ESSnet Big Data II, 17 June 2020. (report).
De Broe, S., Struijs, P., Daas, P., van Delden, A., Burger, J., van den Brakel, J., ten Bosch, O., Zeelenberg, K, Ypma, W.(2020) Updating the Paradigm of Official Statistics: New Quality Criteria for Integrating New Data and Methods in Official Statistics. CBDS-working paper 02-20. Statistics Netherlands, Heerlen/The Hague. paper and online link).
Braaksma, B., Daas, P., Raaijmakers, S., Geurts, A., Meyer-Vitali, A. (2021). AI-Supported Innovation Monitoring. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science, vol 12641. Springer, Cham. pp. 220-226. DOI link. (paper).
Daas, P. and Puts, M. (2020) ISI IASS Webinar on Survey and Big data interactions. Video on GoToStage.com link.
De Broe, S., Struijs, P., Daas, P., van Delden, A., Burger, J., van den Brakel, J., ten Bosch, O., Zeelenberg, K, Ypma, W.(2020) Updating the Paradigm of Official Statistics: New Quality Criteria for Integrating New Data and Methods in Official Statistics. Statistical Journal of the IAOS 37(1), pp. 343-360. DOI-link (paper).
Daas, P., Maslankowski, J., Salgado, D., Quaresma, S., Tuoto, T., Di Consiglio, L., Brancato, G., Righi, P., Six, M., Weinauer, M., Kowarik, A. (2020) Revised version of the Methodological report. Deliverable K9, Workpackage K, ESSnet Big Data II, 17 November 2020. (report).
Daas, P., Puts, M., Maslankowski, J., Salgado, D., Quaresma, S., Tuoto, T., Di Consiglio, L., Brancato, G., Righi, P., Six, M., Kowarik, A. (2020) Report describing the methodological steps of using big data in official statistics with a section on the most important research questions for the future including guidelines. Deliverable K10, Workpackage K, ESSnet Big Data II, 20 November 2020. (report).
Maslankowski, J., Salgado, D., Quaresma, S., Ascari, G., Brancato, G., Di Consiglio, L., Righi, P., Tuoto, T., Daas, P., Weinauer, M., Six, M., Kowarik, A. (2020) Report describing the quality aspects of the different pilots and the way forward. Deliverable K11, Workpackage K, ESSnet Big Data II, 24 November 2020. (report).
Daas, P.J.H., Puts, M.J.H. (2021) Towards Big Data methodology: a generic Big Data based statistical process. Poster for the New Techniques and Technologies for Statistics 2021 conference, Brussels, Belgium. (poster and abstract).
Puts, M.J.H., Daas, P.J.H.. (2021) Unbiased Estimations Based on Binary Classifiers: A Maximum Likelihood Approach. Abstract for the 2021 Symposium on Data Science and Statistics, Machine Learning session. (abstract, paper).
Daas, P.J., de Wolf, N.J. (2021). Identifying different types of companies via their website text. Abstract for the Symposium on Data Science and Statistics (SDSS) 2021, online, USA. (online abstract).
Daas, P.J.H., van der Doef, S. (2021) Using Website Texts to detect Innovative Companies. CBDS discussion paper 01-21. (report and online link).
Daas, P., Schulte Nordholt, E, Tennekes, M., Ossen, S. (2021) Evaluation of the Quality of Administrative Data Used in the Dutch Virtual Census, Chapter 3, pp. 63-82. In: Administrative Records for Survey Methodology, Eds: Chun, A.Y., Larsen, M.D., Durrant, G., Reiter, J.P., Wiley, USA. (DOI link).
Puts, M. and Daas, P. (2021) EMOS Webinar on Machine Learning from a statistical perspective. Video on Youtube.com (video).
De Broe, S., ten Bosch, O., Daas, P., Buiten, G., Laevens, B., Kroese, B. (2021) The need for timely official statistics: the pandemic as a driver for innovation. Paper for the Big Data Matters 3 conference, Statistics Netherlands. (online paper).
Puts, M., Daas, P. (2021) Machine Learning from the Perspective of Official Statistics. The Survey Statistician 84. pp. 12-17. (paper and online link).
Daas, P. (2021) Identifying different types of companies via their website texts. The Summer School on Survey Statistics 2021, On-line keynote speach. Video on Youtube.com (video).
De Broe, S., ten Bosch, O., Daas, P., Buiten, G., Laevens, B., Kroese, B. (2021) The need for timely official statistics: the pandemic as a driver for innovation. Statistical Journal of the IAOS 37(4), pp. 1221-1227. DOI-link (paper).
GOPA team (2021) Data Retrieval. Deliverable 2 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 2).
Daas, P., De Broe, S., ten Bosch, O., Buiten, G., Laevens, B. (2021) The need for timely official statistics: the pandemic as a driver for innovation. Presentation at the UNECE Conference of European Statisticians, Expert Meeting on Statistical Data Collection, 27-30 September, Online. (abstract).
Schaap, E., Mahr, D., Wilms, I., Daas, P. (2021) Are firms within densely connected clusters more innovative? A web approach to inter-firm relationships. Presentation at the 8th Annual World Open Innovation Conference, 9-10 Dec., Eindhoven, the Netherlands. (program).
GOPA team (2022) Classification of Drone company websites. Deliverable 3.1 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 3.1).
GOPA team (2022) Data extraction. Deliverable 3.2 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 3.2).
Daas, P.J.H., de Miguel Moline, B., de Miguel Moline, M. (2022) Searching the Web for the Drone Industry: Classifying Websites in Multiple Countries and Languages with a Single Model. Abstract for the 2022 Symposium on Data Science and Statistics, Applying and Evaluating Logistic Regression Models session. (abstract).
GOPA team (2022) Data extraction, analysis, and visualisation. Deliverable 4 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 4).
Gootzen, Y., Daas, P., van Delden, A. (2022) Quality Framework for combining survey, administrative and big data for official statistics. Paper for the European Conference on Quality in Official Statistics (Q2022), Modernisation of quality frameworks session. (paper).
Daas, P., Tennekes, M., De Miguel, B., De Miguel, M., Santamarina, V., Carausu, F. (2022) Web intelligence for measuring emerging economic trends: the drone industry. Statistical Working Papers, June, Eurostat. (report and online link).
Daas, P.J.H. (2022) Big Data and Official Statistics: Challenges and Applications at Statistics Netherlands. Presentation at the CARMA 2022 conference, Special session on The Internet and Big Data in Official Statistics, June 29-30, Valencia, Spain. (session).
Daas, P. (2022) Big Data and Official Statistics: Challenges and Applications at Statistics Netherlands. Plenary lecture 3 at the 29th Annual Meeting of the Royal Statistical Society of Belgium, Brussels. (abstract).
Daas, P., Maslankowski, J. (2022). Methodological part of the Webinar on Architecture, Quality, and Methodology of the ESSnet Web Intelligence Network (WIN). Online, 23 Nov. 2022. (YouTube link (starts at 34 min.) and slides).
Daas, P.J.H. (2022) Contribution to the Workshop on Methodologies for Official Statistics at ISTAT, Dec. 5-6, Rome, Italy. (web page and slides).
Daas, P.J.H. (2022) Using web site text to identify different types of companies. Presentation at the 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), Dec. 17-19, Londen, UK. (link to book of abstracts, the abstract is on p. 133).
The list of publications continues here.
Daas, P., Jansen, J. (2020) Model degradation in web derived text-based models. Paper for the 3rd International Conference on Advanced Research Methods and Analytics (CARMA), pp 77-84. DOI-link (paper).
Daas, P.J.H., van der Doef, S. (2020) Detecting Innovative Companies via their Website. Statistical Journal of IAOS 36(4), pp. 1239-1251. DOI-link (online link).
Daas, P.J.H. (2020) Developing a Corona sentiment indicator: Methodological justification (in Dutch). CBS-report. (report and online link).
Daas, P., Maslankowski, J., Salgado, D., Quaresma, S., Tuoto, T., Di Consiglio, L., Brancato, G., Righi, P., Six, M., Kowarik, A. (2020) First draft of the Methodological report. Deliverable K5, Workpackage K, ESSnet Big Data II, 17 June 2020. (report).
De Broe, S., Struijs, P., Daas, P., van Delden, A., Burger, J., van den Brakel, J., ten Bosch, O., Zeelenberg, K, Ypma, W.(2020) Updating the Paradigm of Official Statistics: New Quality Criteria for Integrating New Data and Methods in Official Statistics. CBDS-working paper 02-20. Statistics Netherlands, Heerlen/The Hague. paper and online link).
Braaksma, B., Daas, P., Raaijmakers, S., Geurts, A., Meyer-Vitali, A. (2021). AI-Supported Innovation Monitoring. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science, vol 12641. Springer, Cham. pp. 220-226. DOI link. (paper).
Daas, P. and Puts, M. (2020) ISI IASS Webinar on Survey and Big data interactions. Video on GoToStage.com link.
De Broe, S., Struijs, P., Daas, P., van Delden, A., Burger, J., van den Brakel, J., ten Bosch, O., Zeelenberg, K, Ypma, W.(2020) Updating the Paradigm of Official Statistics: New Quality Criteria for Integrating New Data and Methods in Official Statistics. Statistical Journal of the IAOS 37(1), pp. 343-360. DOI-link (paper).
Daas, P., Maslankowski, J., Salgado, D., Quaresma, S., Tuoto, T., Di Consiglio, L., Brancato, G., Righi, P., Six, M., Weinauer, M., Kowarik, A. (2020) Revised version of the Methodological report. Deliverable K9, Workpackage K, ESSnet Big Data II, 17 November 2020. (report).
Daas, P., Puts, M., Maslankowski, J., Salgado, D., Quaresma, S., Tuoto, T., Di Consiglio, L., Brancato, G., Righi, P., Six, M., Kowarik, A. (2020) Report describing the methodological steps of using big data in official statistics with a section on the most important research questions for the future including guidelines. Deliverable K10, Workpackage K, ESSnet Big Data II, 20 November 2020. (report).
Maslankowski, J., Salgado, D., Quaresma, S., Ascari, G., Brancato, G., Di Consiglio, L., Righi, P., Tuoto, T., Daas, P., Weinauer, M., Six, M., Kowarik, A. (2020) Report describing the quality aspects of the different pilots and the way forward. Deliverable K11, Workpackage K, ESSnet Big Data II, 24 November 2020. (report).
Daas, P.J.H., Puts, M.J.H. (2021) Towards Big Data methodology: a generic Big Data based statistical process. Poster for the New Techniques and Technologies for Statistics 2021 conference, Brussels, Belgium. (poster and abstract).
Puts, M.J.H., Daas, P.J.H.. (2021) Unbiased Estimations Based on Binary Classifiers: A Maximum Likelihood Approach. Abstract for the 2021 Symposium on Data Science and Statistics, Machine Learning session. (abstract, paper).
Daas, P.J., de Wolf, N.J. (2021). Identifying different types of companies via their website text. Abstract for the Symposium on Data Science and Statistics (SDSS) 2021, online, USA. (online abstract).
Daas, P.J.H., van der Doef, S. (2021) Using Website Texts to detect Innovative Companies. CBDS discussion paper 01-21. (report and online link).
Daas, P., Schulte Nordholt, E, Tennekes, M., Ossen, S. (2021) Evaluation of the Quality of Administrative Data Used in the Dutch Virtual Census, Chapter 3, pp. 63-82. In: Administrative Records for Survey Methodology, Eds: Chun, A.Y., Larsen, M.D., Durrant, G., Reiter, J.P., Wiley, USA. (DOI link).
Puts, M. and Daas, P. (2021) EMOS Webinar on Machine Learning from a statistical perspective. Video on Youtube.com (video).
De Broe, S., ten Bosch, O., Daas, P., Buiten, G., Laevens, B., Kroese, B. (2021) The need for timely official statistics: the pandemic as a driver for innovation. Paper for the Big Data Matters 3 conference, Statistics Netherlands. (online paper).
Puts, M., Daas, P. (2021) Machine Learning from the Perspective of Official Statistics. The Survey Statistician 84. pp. 12-17. (paper and online link).
Daas, P. (2021) Identifying different types of companies via their website texts. The Summer School on Survey Statistics 2021, On-line keynote speach. Video on Youtube.com (video).
De Broe, S., ten Bosch, O., Daas, P., Buiten, G., Laevens, B., Kroese, B. (2021) The need for timely official statistics: the pandemic as a driver for innovation. Statistical Journal of the IAOS 37(4), pp. 1221-1227. DOI-link (paper).
GOPA team (2021) Data Retrieval. Deliverable 2 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 2).
Daas, P., De Broe, S., ten Bosch, O., Buiten, G., Laevens, B. (2021) The need for timely official statistics: the pandemic as a driver for innovation. Presentation at the UNECE Conference of European Statisticians, Expert Meeting on Statistical Data Collection, 27-30 September, Online. (abstract).
Schaap, E., Mahr, D., Wilms, I., Daas, P. (2021) Are firms within densely connected clusters more innovative? A web approach to inter-firm relationships. Presentation at the 8th Annual World Open Innovation Conference, 9-10 Dec., Eindhoven, the Netherlands. (program).
GOPA team (2022) Classification of Drone company websites. Deliverable 3.1 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 3.1).
GOPA team (2022) Data extraction. Deliverable 3.2 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 3.2).
Daas, P.J.H., de Miguel Moline, B., de Miguel Moline, M. (2022) Searching the Web for the Drone Industry: Classifying Websites in Multiple Countries and Languages with a Single Model. Abstract for the 2022 Symposium on Data Science and Statistics, Applying and Evaluating Logistic Regression Models session. (abstract).
GOPA team (2022) Data extraction, analysis, and visualisation. Deliverable 4 of the Web Intelligence for Measuring Emerging Economic Trends: the Drone Industry project. Joint project of GOPA, Statistics Netherlands and Universitat Politècnica de València. (deliverable 4).
Gootzen, Y., Daas, P., van Delden, A. (2022) Quality Framework for combining survey, administrative and big data for official statistics. Paper for the European Conference on Quality in Official Statistics (Q2022), Modernisation of quality frameworks session. (paper).
Daas, P., Tennekes, M., De Miguel, B., De Miguel, M., Santamarina, V., Carausu, F. (2022) Web intelligence for measuring emerging economic trends: the drone industry. Statistical Working Papers, June, Eurostat. (report and online link).
Daas, P.J.H. (2022) Big Data and Official Statistics: Challenges and Applications at Statistics Netherlands. Presentation at the CARMA 2022 conference, Special session on The Internet and Big Data in Official Statistics, June 29-30, Valencia, Spain. (session).
Daas, P. (2022) Big Data and Official Statistics: Challenges and Applications at Statistics Netherlands. Plenary lecture 3 at the 29th Annual Meeting of the Royal Statistical Society of Belgium, Brussels. (abstract).
Daas, P., Maslankowski, J. (2022). Methodological part of the Webinar on Architecture, Quality, and Methodology of the ESSnet Web Intelligence Network (WIN). Online, 23 Nov. 2022. (YouTube link (starts at 34 min.) and slides).
Daas, P.J.H. (2022) Contribution to the Workshop on Methodologies for Official Statistics at ISTAT, Dec. 5-6, Rome, Italy. (web page and slides).
Daas, P.J.H. (2022) Using web site text to identify different types of companies. Presentation at the 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), Dec. 17-19, Londen, UK. (link to book of abstracts, the abstract is on p. 133).
The list of publications continues here.