List of Publications
Statistics Netherlands publication period (2023 - current)
Links to other publications 1, 2, 3, 4, 5, 6, 7.
Daas, P.J.H., De Miguel, B., De Miguel, M. (2023) Identifying Drone Web Sites in Multiple Countries and Languages with a Single Model. Journal of Data Science 21(2), 225-238. DOI-link (paper).
Daas, P., Verweij, R., Evenhuis, P., Dijkstra, K., Bleker, S. (2023) Validation of the Social Tension Indicator (in Dutch). Statistics Netherlands report, The Hague/Heerlen, The Netherlands.6 (report and online link).
Daas, P., Hassink, W., Klijs, B. (2023) On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms. IZA - Institute of Labor Economics discussion paper 15941, February. (report and online link).
Daas, P., Puts, M. (2023) Lessons learned when applying Machine Learning in Official Statistics: Why it helps to be a survey statistician and a data scientist! Presentation for the UNECE Machine Learning for Official Statistics Workshop 2023, Geneva, Switzerland. (Link to abstract, paper, online link of paper, and presentation).
Gootzen, Y.A.P.M., Daas, P.J.H., van Delden, A. (2023) Quality Framework for combining survey, administrative and big data for official statistics. Statistical Journal of the IAOS 39(2), 439-466. DOI-link).
Cremers, D., Daas, P., Polder, M., van Roekel, R., Vancauteren, M., Visser, C. (2023) The Impact of innovation on Firm Performance: The Role of Micro-firms in the Netherlands. Paper and presentation at the Economic Statistics Centre of Excellence (ESCoE) conference on Economic Measurement 2023, May 18, Londen, UK.
Daas, P. (2023) Big Data in Official Statistics. Inaugural speech at the Eindhoven University of Technology, The Netherlands, May 26. (lecture, online link, slides, and the recorded stream).
Daas, P. (2023) Prof. Piet Daas: 'Using the opportunities of big data for official statistics' (in Dutch). Statistics Netherlands interview, May. (CBS web page link and Computable link).
Daas, P., Tennekes, M., de Miguel Molina, B., de Miguel Molina, M., Santamarina Campos, V., Carausu, F., Ascheri, A. (2023) Landscaping the Drone Industry on the Web. Presentation for the Web Intelligence Hub Conference (WIH-CON), June 12, Brussel, Belgium. The presentation can be viewed after registration on the conference page.
Daas, P. (2023) Categorizing company websites based on website texts. Presentation at the ISI 2023 World Statistics Congress, 16-20 July, Ottawa, Canada. (abstract).
Daas, P.J.H. (2023) Contribution to the Second Workshop on Methodologies for Official Statistics at ISTAT, Dec. 6-7, Rome, Italy. (web page).
Daas, P., Maslankowski, J. (2023) Current challenges and possible big data solutions for the use of web data as a source for official statistics, Wiadomosci Statystyczne/The Polish Statistician 68(12), 49-64. DOI-link (local and online link).
Daas, P., Hassink, W., Klijs, B. (2024) On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms. Journal of Official Statistics, 40(1), 190-211. DOI-link.(local and online link).
Puts, M., Daas, P. (2024) Leveraging Machine Learning for Official Statistics: Methodology and Application. Keynote speech at the Conference on Foundations and Advances of Machine Learning in Official Statistics, 3rd to 5th April, Wiesbaden, Germany. (abstract and slides).
Gubbels, L., Puts, M., Daas, P. (2024) Bias Correction in Machine Learning-based Classification of Rare Events. Presentation for Symposium on Data Science and Statistics (SDSS) 2024, Statistical Data Science track, Classification and Modeling session, Richmond, VA, USA. (abstract, presentation, and paper).
Urban Data Centre Eindhoven (2024) Creative Industry in Eindhoven (in Dutch). (Web publication).
Web Intelligence Network Blog (2024) Navigating the Digital Frontier: Unlocking the Potential of Web Scraped Data for Official Statistics. (blog link).
Puts, M., Daas, P. (2024) Alternative Sources and Machine Learning for Official Statistics: A Review. Journal of Official Statistics, 40(2), 348-351. DOI-link.(local and online link).
The list of publications starts here. My presentations are also shared on SlideShare.
Daas, P.J.H., De Miguel, B., De Miguel, M. (2023) Identifying Drone Web Sites in Multiple Countries and Languages with a Single Model. Journal of Data Science 21(2), 225-238. DOI-link (paper).
Daas, P., Verweij, R., Evenhuis, P., Dijkstra, K., Bleker, S. (2023) Validation of the Social Tension Indicator (in Dutch). Statistics Netherlands report, The Hague/Heerlen, The Netherlands.6 (report and online link).
Daas, P., Hassink, W., Klijs, B. (2023) On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms. IZA - Institute of Labor Economics discussion paper 15941, February. (report and online link).
Daas, P., Puts, M. (2023) Lessons learned when applying Machine Learning in Official Statistics: Why it helps to be a survey statistician and a data scientist! Presentation for the UNECE Machine Learning for Official Statistics Workshop 2023, Geneva, Switzerland. (Link to abstract, paper, online link of paper, and presentation).
Gootzen, Y.A.P.M., Daas, P.J.H., van Delden, A. (2023) Quality Framework for combining survey, administrative and big data for official statistics. Statistical Journal of the IAOS 39(2), 439-466. DOI-link).
Cremers, D., Daas, P., Polder, M., van Roekel, R., Vancauteren, M., Visser, C. (2023) The Impact of innovation on Firm Performance: The Role of Micro-firms in the Netherlands. Paper and presentation at the Economic Statistics Centre of Excellence (ESCoE) conference on Economic Measurement 2023, May 18, Londen, UK.
Daas, P. (2023) Big Data in Official Statistics. Inaugural speech at the Eindhoven University of Technology, The Netherlands, May 26. (lecture, online link, slides, and the recorded stream).
Daas, P. (2023) Prof. Piet Daas: 'Using the opportunities of big data for official statistics' (in Dutch). Statistics Netherlands interview, May. (CBS web page link and Computable link).
Daas, P., Tennekes, M., de Miguel Molina, B., de Miguel Molina, M., Santamarina Campos, V., Carausu, F., Ascheri, A. (2023) Landscaping the Drone Industry on the Web. Presentation for the Web Intelligence Hub Conference (WIH-CON), June 12, Brussel, Belgium. The presentation can be viewed after registration on the conference page.
Daas, P. (2023) Categorizing company websites based on website texts. Presentation at the ISI 2023 World Statistics Congress, 16-20 July, Ottawa, Canada. (abstract).
Daas, P.J.H. (2023) Contribution to the Second Workshop on Methodologies for Official Statistics at ISTAT, Dec. 6-7, Rome, Italy. (web page).
Daas, P., Maslankowski, J. (2023) Current challenges and possible big data solutions for the use of web data as a source for official statistics, Wiadomosci Statystyczne/The Polish Statistician 68(12), 49-64. DOI-link (local and online link).
Daas, P., Hassink, W., Klijs, B. (2024) On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms. Journal of Official Statistics, 40(1), 190-211. DOI-link.(local and online link).
Puts, M., Daas, P. (2024) Leveraging Machine Learning for Official Statistics: Methodology and Application. Keynote speech at the Conference on Foundations and Advances of Machine Learning in Official Statistics, 3rd to 5th April, Wiesbaden, Germany. (abstract and slides).
Gubbels, L., Puts, M., Daas, P. (2024) Bias Correction in Machine Learning-based Classification of Rare Events. Presentation for Symposium on Data Science and Statistics (SDSS) 2024, Statistical Data Science track, Classification and Modeling session, Richmond, VA, USA. (abstract, presentation, and paper).
Urban Data Centre Eindhoven (2024) Creative Industry in Eindhoven (in Dutch). (Web publication).
Web Intelligence Network Blog (2024) Navigating the Digital Frontier: Unlocking the Potential of Web Scraped Data for Official Statistics. (blog link).
Puts, M., Daas, P. (2024) Alternative Sources and Machine Learning for Official Statistics: A Review. Journal of Official Statistics, 40(2), 348-351. DOI-link.(local and online link).
The list of publications starts here. My presentations are also shared on SlideShare.