Dr. A. (Andreas) Alfons
A. Alfons, C. Croux & S.E.C. Gelper (2016). Robust groupwise least angle regression. Computational Statistics & Data Analysis, 93, 421-435. doi: http://dx.doi.org/10.1016/j.csda.2015.02.007
A. Alfons, C. Croux & P. Filzmoser (2016). Robust maximum association between data sets: The R package ccaPP. Austrian Journal of Statistics, 45 (1), 71-79. doi: http://www.ajs.or.at/index.php/ajs/article/view/vol45-1-5
V. Öllerer, C. Croux & A. Alfons (2015). The influence function of penalized regression estimators. Statistics, 49 (4), 741-765. doi: 10.1080/02331888.2014.922563
A. Alfons & M. Templ (2013). Estimation of social exclusion indicators from complex surveys: The R package laeken. Journal of Statistical Software, 54 (15), 1-25. doi: 10.18637/jss.v054.i15
A. Alfons, C. Croux & S.E.C. Gelper (2013). Sparse least trimmed squares regression for analyzing high-dimensional large data sets. Annals of Applied Statistics, 7 (1), 226-248. doi: http://dx.doi.org/10.1214/12-AOAS575[go to publisher's site]
A. Alfons, M. Templ & P. Filzmoser (2013). Robust estimation of economic indicators from survey samples based on Pareto tail modeling. Journal of the Royal Statistical Society. Series C, Applied Statistics, 62 (2), 271-286. doi: 10.1111/j.1467-9876.2012.01063.x
M. Templ, A. Alfons & P. Filzmoser (2012). Exploring incomplete data using visualization techniques. Advances in Data Analysis and Classification, 6 (1), 29-47. doi: 10.1007/s11634-011-0102-y
A. Alfons, W.E. Baaske, P. Filzmoser, W. Mader & R. Wieser (2011). Robust variable selection with application to quality of life research. Statistical Methods and Applications, 20 (1), 65-82. doi: 10.1007/s10260-010-0151-y
A. Alfons, S. Kraft, M. Templ & P. Filzmoser (2011). Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods and Applications, 20 (3), 383-407. doi: 10.1007/s10260-011-0163-2
A. Alfons, M. Templ & P. Filzmoser (2010). An object-oriented framework for statistical simulation: The R package simFrame. Journal of Statistical Software, 37 (3), 1-36. doi: 10.18637/jss.v037.i03
A. Alfons, M. Templ & P. Filzmoser (2010). Contamination models in the R package simFrame for statistical simulation. In S. Aivazian, P. Filzmoser & Y. Kharin (Eds.), Computer Data Analysis and Modeling: Complex Stochastic Data and Systems, Volume 2 (pp. 178-181)
A. Alfons (2011). Simulation and Robust Statistics: Application to Laeken Indicators and Quality of Life Research. Saarbrücken: Südwestdeutscher Verlag für Hochschulschriften
Book Contributions (2)
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
PhD Track (1)
PhD Vacancy (1)
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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