Michael Wolf (statistician)

Michael Wolf
Born (1967-06-01) June 1, 1967
Nationality  Germany
Fields Econometrics, Statistics
Institutions University of Zurich
Alma mater Stanford University
Known for Shrinkage Estimation of Covariance Matrices; Subsampling; and Multiple Testing.
Website
www.econ.uzh.ch/faculty/wolf.html

Michael Wolf (born June 1, 1967) holds the Chair of Econometrics and Applied Statistics in the Department of Economics at the University of Zurich, Switzerland. He was previously Professor at UCLA, Charles III University of Madrid, and Pompeu Fabra University.

Biography

Michael Wolf was born in Germany, where he obtained a Bachelor's Degree in Mathematics from the University of Augsburg. From 1991 he studied Statistics at Stanford University (M.Sc. 1995, Ph.D. 1996).

Michael Wolf is known for his work on shrinkage estimation of large-dimensional covariance matrices. While originally motivated by Markowitz portfolio selection, the linear shrinkage estimator he developed in collaboration with Olivier Ledoit has been widely adopted by other researchers in a variety of scientific fields such as cancer research,[1] chemistry,[2] civil engineering,[3] climatology,[4] electrical engineering,[5] genetics,[6] geology,[7] neuroscience,[8] psychology,[9] speech recognition,[10] etc. The common feature between these applications is that the dimension of the covariance matrix is not negligible with respect to the size of the sample. In this case the usual estimator, the sample covariance matrix, is inaccurate and ill-conditioned. By contrast, the Ledoit-Wolf estimator is more accurate and guaranteed to be well-conditioned, even in the difficult case where matrix dimension exceeds sample size.

Michael Wolf also co-wrote the book on Subsampling, a resampling method inspired by the jackknife that constitutes an alternative to the bootstrap. In the field of multiple testing, he introduced new procedures to control the familywise error rate that are more powerful than previous methods, by taking into account the dependence between test statistics. He also worked on procedures to control generalized error rates, such as the generalized familywise error rate, the false discovery proportion and the false discovery rate.

Michael Wolf served on the editorial board of the Annals of Statistics and Statistics and Probability Letters. In 2004, he won the Distinguished Researcher award from the Generalitat of Catalonia. He was invited to give the prestigious Gumbel Lecture at the Annual Meeting of the German Statistical Society in Cologne in 2008.

Selected publications

Books

Articles on Shrinkage Estimation of Covariance Matrices

Articles on Subsampling

Articles on Multiple Testing

References

  1. Pyeon, Dohun; Newton, Michael A.; Lambert, Paul F.; den Boon, Johan A.; Sengupta, Srikumar; Marsit, Carmen J.; Woodworth, Craig D.; Connor, Joseph P.; Haugen, Thomas H.; Smith, Elaine M.; Kelsey, Karl T.; Turek, Lubomir P.; Ahlquist, Paul (May 2007). "Fundamental differences in cell cycle deregulation in human papillomavirus–positive and human papillomavirus–negative head/neck and cervical cancers" (PDF). Cancer Research 67 (10): 4605–4619. doi:10.1158/0008-5472.CAN-06-3619.
  2. Guo, Syuan-Ming; He, Jun; Monnier, Nilah; Sun, Guangyu; Wohland, Thorsten; Bathe, Mark (March 2012). "Bayesian approach to the analysis of fluorescence correlation spectroscopy data II: Application to simulated and in vitro data". Analytical Chemistry 84 (9): 3880−3888. doi:10.1021/ac2034375.
  3. Michaelides, Pavlos G.; Apostolellis, Panagiotis G.; Fassois, Spilios D. (July 2011). Vibration–based damage diagnosis in a laboratory cable–stayed bridge model via an RCP–ARX model based method (PDF). Proceedings of the 9th International Conference on Damage Assessment of Structures (DAMAS 2011). Journal of Physics: Conference Series. Oxford, United Kingdom: Institute of Physics. p. 012104. doi:10.1088/1742-6596/305/1/012104.
  4. Ribes, Aurélien; Azaïs, Jean-Marc; Planton, Serge (October 2009). "Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate" (PDF). Climate Dynamics 33 (5): 707–722. doi:10.1007/s00382-009-0561-4.
  5. Wei, Zhe; Huang, Jianguo; Hui, Yuejiao (September 2011). Adaptive-beamforming-based multiple targets signal separation (PDF). Proceedings of the 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). Xi'an, China: Institute of Electrical and Electronics Engineers. pp. 1–4. doi:10.1109/ICSPCC.2011.6061572.
  6. Lin, Ja-an; Zhu, Hongtu; Knickmeyer, Rebecca; Styner, Martin; Gilmore, John; Ibrahim, Joseph G. (September 2012). "Projection regression models for multivariate imaging phenotype" (PDF). Genetic Epidemiology 36 (6): 631–641. doi:10.1002/gepi.21658. PMC 3418398. PMID 22807230.
  7. Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim (June 2013). "An iterative stochastic ensemble method for parameter estimation of subsurface flow models" (PDF). Journal of Computational Physics 242: 696–714. doi:10.1016/j.jcp.2013.01.047.
  8. Varoquaux, Gaël; Gramfort, Alexandre; Poline, Jean-Baptiste; Thirion, Bertrand (September 2012). "Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?" (PDF). Journal of Physiology - Paris 106 (5): 212–221. doi:10.1016/j.jphysparis.2012.01.001.
  9. Markon, Kristian E. (February 2010). "Modeling psychopathology structure: a symptom-level analysis of Axis I and II disorders" (PDF). Psychological Medicine 40 (02): 273–288. doi:10.1017/S0033291709990183.
  10. Bell, Peter; King, Simon (November 2009). Diagonal priors for full covariance speech recognition (PDF). Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). Merano, Italy: Institute of Electrical and Electronics Engineers. pp. 113–117. doi:10.1109/ASRU.2009.5373344.

External links

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