Dr. A. (Andreas) Alfons

Erasmus School of Economics (ESE)
Erasmus University Rotterdam
Associate Member ERIM
Field: Marketing
Affiliated since 2013
  • A. Alfons (2011). Simulation and Robust Statistics: Application to Laeken Indicators and Quality of Life Research. Saarbrücken: Südwestdeutscher Verlag für Hochschulschriften
  • A. Alfons, M. Templ, P. Filzmoser & J. Holzer (2010). A comparison of robust methods for Pareto tail modeling in the case of Laeken indicators. In C. Borgelt, G. González-Rodríguez, W. Trutschnig, M.A. Lubiano, M.A. Gil, P. Grzegorzewski & O. Hryniewicz (Eds.), Combining Soft Computing and Statistical Methods in Data Analysis (Advances in Intelligent and Soft Computing, 77) (pp. 17-24). Heidelberg: Springer
  • M. Templ & A. Alfons (2010). Disclosure risk of synthetic population data with application in the case of EU-SILC. In J. Domingo-Ferrer & E. Magkos (Eds.), Privacy in Statistical Databases (Lecture Notes in Computer Science, 6344) (pp. 174-186). Heidelberg: Springer
  • Role: Co-promotor
  • PhD Candidate: Gertjan van den Burg
  • Time frame: 2012 - 2018

Past
  • Introduction to Data Analysis with R (2017/2018, 2016/2017)

Rating-scale data are omnipresent in modern data collection, yet statistical methods typically do not take into account the peculiarities of such data. In addition, while the literature on robust (outlier-resistant) methods for continuous data is growing rapidly, such techniques are not suitable for rating-scale data due to their discrete nature and limited range. This project aims to fill those gaps in the statistical literature by developing robust methods for the analysis of rating-scale data.

Empirical research in the social sciences relies heavily on the statistical analysis of data measured on rating scales. Results of such research have a tremendous impact on society: for example, policy makers monitor opinions in society via rating-scale data collected in surveys, psychologists collect rating-scale data in experiments to gain new insights into human behavior, and companies let test groups rate various aspects of a new product before launching it.

Due to modern technology and the internet, data collection is easier than ever. As a result, data sets are growing ever larger but at the same time reliability of the data is decreasing with online data collection. Even though rating scales by definition have a limited range and thus do not exhibit extreme values, observations can still be outliers if they go against the correlation structure of the majority of the data. Such correlation outliers are likely to be present, especially in big data. Yet currently the literature on outliers in rating-scale data is very scarce.

The aim of this project is to develop outlier-resistant methods for the statistical analysis of rating-scale data, as well as to extract the relevant information from big (incomplete) rating-scale data.

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2018
December
06
Research Seminar
As: Coordinator
2018
November
29
Research Seminar
As: Coordinator
2018
November
15
Research Seminar
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2018
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01
2018
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25
2018
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18
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2018
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04
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As: Coordinator
2018
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04
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2018
May
15
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As: Coordinator
2018
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03
2018
April
12
2018
March
15
Research Seminar
As: Coordinator
2018
February
08
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As: Coordinator
2017
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12
2017
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16
2017
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26
2017
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24
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2017
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12
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2016
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08
2016
November
10
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2016
September
22
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2016
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26
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2016
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11
2016
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03
2015
December
03
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As: Coordinator
2015
December
03
Research Seminar
As: Contact, Coordinator
2015
November
12
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As: Coordinator
2015
November
05
2015
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29
Research Seminar
As: Coordinator, Contact
2015
October
15
Research Seminar
As: Coordinator
2015
September
10
Research Seminar
As: Coordinator, Contact

Address

Visiting address

Office: Tinbergen Building H11-21
Burgemeester Oudlaan 50
3062 PA Rotterdam

Postal address

Postbus 1738
3000 DR Rotterdam
Netherlands