FEATURED AUTHOR
Henrik Madsen
He graduated with a PhD in Statistics at the Technical University of Denmark in 1986. He was appointed Ass. Prof. in Statistics in 1986, Assoc. Prof. in 1989, and Professor in Mathematical Statistics with a focus on Stochastic Dynamic Systems in 1999. lected member of ISI/Bernoulli Society. He has authored or co-authored approximately 500 papers (about 170 journal papers), and 12 books.
Biography
He graduated with a MS and PhD in Statistics at the TechnicalUniversity of Denmark in 1982 and 1986, respectively. He was
appointed Ass. Prof. in Statistics in 1986, Assoc. Prof. in 1989, and Professor in Mathematical Statistics with a special focus on
Stochastic Dynamic Systems in 1999. He is an elected member of the
International Statistical Institute (ISI).
His main research interest is related to analysis and modelling of
stochastic dynamics systems. This includes signal processing, time
series analysis, identification, estimation, grey-box modelling,
prediction, optimization and control. The applications are mostly
related to Energy Systems, Informatics, Environmental Systems,
Bioinformatics, Biostatistics, Process Modelling and Finance. He has authored or co-authored approximately 500 papers and technical
reports, and about 12 books. The two most recent books are: 'Time
Series Analysis' (2008) and 'General and Generalized Linear Models'
(2010), both on Chapman & Hall. He is the author of a new book
'Forecasting Wind Power Generation', which will be published in 2013.
Since 1992 he has been the leader of one of the most active research
groups in Europe in relation to wind power and price forecasting and
integration of renewables in power systems. Today methods and tools
originating from this research group are crucial for operating the
Danish power system where wind power accounts for more than 25 pct of the total power production.
Education
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PhD in Math Statistics, DTU, Lyngby, 1986
Areas of Research / Professional Expertise
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His main research interest is related to analysis and modelling of
stochastic dynamics systems. This includes signal processing, time
series analysis, identification, estimation, grey-box modelling,
prediction, optimization and control. The applications are mostly
related to Energy Systems, Informatics, Environmental Systems,
Bioinformatics, Biostatistics, Process Modelling and Finance.
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Books
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