Gerda Claeskens

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Gerda Claeskens is a Belgian statistician. She is a professor of statistics in the Faculty of Economics and Business at KU Leuven, associated with the KU Research Centre for Operations Research and Business Statistics (ORSTAT). [1]

Contents

Contributions

Claeskens is an expert in nonparametric statistics and in model selection, including model averaging. She is known for developing, with Nils Lid Hjort, the focused information criterion for model selection. With Hjort, she is the author of the book Model Selection and Model Averaging (Cambridge University Press, 2008). [2]

Education and career

Claeskens earned a licentiate in mathematics at the University of Antwerp in 1995. In 1999, she earned a master's degree in biostatistics and Ph.D. in mathematics, at Limburgs Universitair Centrum (now the University of Hasselt); [3] her dissertation, supervised by Marc Aerts, was Smoothing Techniques and Bootstrap Methods for Multiparameter Likelihood. [3] [4] She did postdoctoral research at the Australian National University. Afterwards, she was an assistant professor at the Eindhoven University of Technology (from 1999 to 2000) and at Texas A&M University (from 2000 to 2004). She joined the KU Leuven faculty in 2004, and was promoted to full professor there in 2012. [3]

Recognition

In 2004, Claeskens won the Noether Young Scholar Award of the American Statistical Association. [5] She is an elected member of the International Statistical Institute. [6] In 2012 she was elected as a Fellow of the Institute of Mathematical Statistics, and in 2016 she was a Medallion Lecturer for the Institute, speaking about her work on model selection and model averaging. [5] In 2019 she was elected as a Fellow of the American Statistical Association. [7]

Related Research Articles

In statistics, the focused information criterion (FIC) is a method for selecting the most appropriate model among a set of competitors for a given data set. Unlike most other model selection strategies, like the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the deviance information criterion (DIC), the FIC does not attempt to assess the overall fit of candidate models but focuses attention directly on the parameter of primary interest with the statistical analysis, say , for which competing models lead to different estimates, say for model . The FIC method consists in first developing an exact or approximate expression for the precision or quality of each estimator, say for , and then use data to estimate these precision measures, say . In the end the model with best estimated precision is selected. The FIC methodology was developed by Gerda Claeskens and Nils Lid Hjort, first in two 2003 discussion articles in Journal of the American Statistical Association and later on in other papers and in their 2008 book.

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References

  1. ↑ "Gerda Claeskens", KU Leuven Who's Who, KU Leuven, retrieved 2017-11-24
  2. ↑ Reviews of Model Selection and Model Averaging:
  3. 1 2 3 Curriculum vitae (PDF), retrieved 2017-11-24
  4. ↑ Gerda Claeskens at the Mathematics Genealogy Project
  5. 1 2 "Medallion lecture preview: Gerda Claeskens", IMS Bulletin, Institute of Mathematical Statistics, March 31, 2016
  6. ↑ Individual members, International Statistical Institute, archived from the original on 2017-07-29, retrieved 2017-11-24
  7. ↑ ASA Fellow Announcement (PDF), American Statistical Association, retrieved 2019-05-12


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