Statistical Information and Modelling (SIM)
Scientific advisors
1. Introduction
3. Description of the research themes
3.1
Statistical Signal and Image Analysis
3.2 Statistics in Biology
3.3 Statistics in
Industry
5. Former people and past activities
1. Introduction
Mathematical Statistics is an indispensable tool in all fields of modern
science. At EURANDOM we focus on themes from various areas presently undergoing
vigorous development, and supplying major challenges to statistics: data, signal
and image analysis, life sciences, computational learning, industry, quantum information. Each area presents its
own unique types of problem, but the same fundamental ideas from theoretical
statistics can be applied in all, giving insight and creating underlying links.
The availability of huge amounts of data, having a complex stochastic structure
depending on very many unknown parameters, calls for statistical modelling and
analysis techniques having a different flavour from classical methodology.
Despite modern computational power, the problems require a closer than ever
intertwining of algorithms and theory: scientific ambition and the size and
complexity of data grow faster than our ability to mechanically process those
same data. Statistical optimality and computational feasibility cannot both be
achieved at the same time; compromises need to be taken and the guiding
principles of classical statistical theory do not necessarily lead to useful
solutions. Still, we need to capitalize more than ever on what we have learnt
from classical statistical theory, and in particular from asymptotic (large
sample) optimality theory.
Underlying and unifying mathematical statistical themes in these areas are:
 highdimensional statistical modelling
 Bayesian methodology (studied from frequentist perspectives)
 empirical process theory
 asymptotic optimality
 missing data problems and hidden Markov models
 experimental design
 algebraic and geometric methods
 statistical information
 networks.
By concentrating on problems which are in a wider sense linked to nonparametric methods in statistics the SIMgroup aims at developing a high degree of cohesion allowing scientific contacts on an everyday basis between all members of the group. On the other hand topics like signal extraction (time series), image analysis and statistical learning are sufficiently broad to make it possible to include projects from different disciplines.
Our current research themes are:
Statistical Signal and Image Analysis
Statistics in Biology
Statistics in
Industry (algebraic methods, reliability)
Related to industrial statistics, but managed separately from SIM, is the Integrated Batteries project.
In addition there is activity in the theoretical aspects of statistical learning, that plays a role in each of the above themes, and in quantum information (optimal quantum measurement).We close this section with a discussion on these activities; the three themes are described in Section 3.
Statistical learning is an inspiring interdisciplinary field, with
large components in both computer science and mathematical statistics. Reliance
on a properly specified stochastic model is much less strong than in classical
statistics; rather, one focuses on a welldefined task (e.g., a prediction task)
and on a specific loss function, and one develops procedures with approximately
optimal longrun behaviour independently of the correct model.
More specialised, also statistics in quantum information influences important
questions in statistics. In quantum information, quantum systems (for instance, single photons) are
regarded as carriers of information and are used for communication and data
processing tasks. This always involves measurement of quantum systems, and using
the measurement outcomes to make inference about the state of the system.
Typically one can choose between different, incompatible measurements. "Quantum
Statistics" is as much concerned with the optimal choice of measurement as with
the optimal processing of the data obtained from a given measurement. Within SIM
research focusses on asymptotic theory. In that case, the optimal data
processing, given the design of the experiment, can to a large extent be left to
classical statistical theory, though often the models are new to statistics and
can present new challenges. For instance, quantum tomography can be presented as
a variant of classical statistical tomography problems from medical imaging, but
prior knowledge about the image to be reconstructed is of a completely different
nature in the two domains.
If the sample is going to be large but the experiment is yet to be designed,
then we can focus on designing it to optimise the statistical information in the data,
as measured by Fisher information. Still, quantum complementarity means that one
can only obtain much information about one parameter at the expense of only
learning little about another. Moreover, the optimal experiment will depend on
the value of the parameter, which is unknown, and hence one must look at adaptive
procedures. Experiments in quantum physics are often done simply to prove
the incompatibility of what we see in the laboratory, with a classical
(prequantum) description of reality. Here again one can pose the question what
is the best experiment to do, in order to obtain the expected evidence against
classical physics, as speedily as possible.
The Statistical Information and Modelling programme runs in close collaboration with mathematical statisticians of the stochastics groups at the Vrije Universiteit of Amsterdam, the Universities of Amsterdam, Utrecht, and the Eindhoven University of Technology.
Throughout the year, EURANDOM has openings for postdoc, PhD, sabbatical and visiting positions. Those interested in such positions are kindly asked to send their application to:
Professor O.J. Boxma  Scientific director
EURANDOM
P.O. Box 513
5600 MB Eindhoven,
The Netherlands
For further
information about the program and the positions, please contact one of the
advisors of the theme of your interest (see section 3) at:
EURANDOM
P.O. Box 513
5600 MB Eindhoven
The Netherlands
List of present Postdocs, PhD students and Research Fellows
Name 
Postdoc/PhD student/ Research Fellow 
Period 
Dmitry Danilov 
Postdoc 
05/2006  05/2008 
Ambedkar Dukkipatti  Postdoc  04/2007  04/2008 
Peter Grünwald 
Research Fellow 
01/2005  01/2006 
Efang Kong 
Postdoc 
02/2007  02/2009 
Alexander Ledovskikh  Postdoc  09/2007  04/2009 
Guangming Pan 
Postdoc 
06/2007  06/2009 
Shota Gugushvili 
Postdoc 
01/2008  01/2010 
Steering Committee
An international steering committee oversees the SIMprogramme:
 P. Donnelly 
University of Oxford, United Kingdom
 P. Green
 University of Bristol, United Kingdom

U. Gather  University of Dortmund, Germany
 M. Newby
 City University, United Kingdom

S. Tavaré
 University of South Carolina, United States of America
 A.
Tsybakov  Université Paris VI, France
Various activities
 Informal meeting Eindhoven statisticians  biweekly. Organisers:
S. Kuhnt and
S. Di Bucchianico
Reports 2006
For more information about reports of the current year have a look at the EURANDOM reports page. On this page you will also find downloads of abstracts and reports.
2006001
Minimax and adaptive estimation of the Wigner function in quantum
homodyne tomography with noisy data
L. Artiles, C. Butucea, M. Guta
2006002
Penalized empirical risk minimalization
L. Mohammadi
2006013
Estimation of the reaction efficiency in Polymerase Chain Reaction
N. Lalam
2006018
Pseudo maximum likelihood estimation for differential equations
N. Lalam, C. Klaassen
2006023
Factorial Designs and Harmonic Analysis on Finite Abelian Groups
P. van de Ven, A. Di Bucchianico
2006030
Title t.b.a.
F. Rigat
2006034
Bulletproof math
L. Mohammadi,
2006035
On nonnegative garrote estimator in a linear regression model
L. Mohammadi
3. Description of the research themes
3.1 Statistical Signal and Image Analysis
Scientific advisors:
 P.L. Davies (TU/e and University of Duisburg  Essen, Germany)
 M.N.M. van Lieshout (CWI, Amsterdam)
Research is concentrated on high dimensional or infinite dimensional parameter spaces as they occur in nonparametric regression and signal extraction for nonlinear systems. The emphasis is on signal extraction for time series and the analysis of two and three dimensional images but related topics from other areas which also involve high dimensional spaces will be included. Work in these areas combine theoretical considerations which clarify the performance of the procedures by subjecting them to a mathematical analysis as well as the development and implementation of algorithms so that they can be applied to data sets found in practice. An important and as yet little developed area is the asymptotic analysis of algorithms as an increasing number of procedures are defined in terms of an algorithm rather than as the solution of some extremal problem.
The research group works on lowlevel denoising, intermediate level segmentation algorithms and benchmarking, as well as highlevel image and video interpretation problems. The methodologies used vary widely from splines to linear and quadratic programming problems, and automatic smoothing using diffusion equations. We use tools and concepts from stochastic geometry, such as marked point and object processes, and Markov chain Monte Carlo theory and methods. The problems in image analysis are many and varied from identifying peaks and edges to linear and nonlinear inverse problems. Signal extraction concentrates on times series both in one and several dimensions with applications in fields from financial data to the online monitoring of intensive care patients. Here again the development and implementation of algorithms will be of great importance with in some cases the emphasis being on speed for online application rather than the impractical calculation of some optimal statistic. Methods from robust statistics have a role to play in both areas.
The group has good contacts to other colleagues working on related problems: A.J. Baddeley (University of Western Australia, Australia), X. Descombes (INRIA Sophia Antipolis, France), L. Dümbgen (University of Berne, Switzerland), U. Gather (University of Dortmund, Germany), P. Gregori (University Jaume 1, Spain), O. Häggström (Chalmers University of Technology, Sweden), U. Hahn (University of Augsburg, Germany), R. Huele (Leiden), E.B.V. Jensen (University of Aarhus, Denmark), W.S. Kendall (University of Warwick, UK), R. Kluszczyński (Nicolaus Copernicus University, Poland) A. Kovac (University of Bristol, U.K.), V. Liebscher (University of Greifswald, Germany), J. Mateu (University Jaume I, Spain), J. Möller (University of Aalborg, Denmark), I.S. Molchanov (University of Berne, Switzerland), E.J. Pebesma (Utrecht), T. Schreiber (Nicolaus Copernicus University, Poland), V. Spokoiny (Weierstrass Institute, Berlin, Germany), A. Stein (Wageningen), R.S. Stoica (INRA Avignon, France), E. Thönnes (University of Warwick, UK), G. Winkler (GSF, Munich, Germany), J. Zerubia (INRIA Sophia Antipolis, France), S.A. Zuyev (University of Strathclyde, UK), and E.W. van Zwet (Leiden).
Scientific advisors:
 M.C.M. de Gunst (Vrije Universiteit, Amsterdam)
 C.A.J. Klaassen (University of Amsterdam)
Molecular biology, genetics, cell biology, and systems biology generate
enormous challenges for statisticians in the 21st century, as scientists try
better and better to understand the pathways from DNA to living organism. At
EURANDOM we work closely with biologists in a number of concrete research
projects. The emphasis lies on stochastic modelling and on the interplay of
theory and application. At present the main topics are:
Statistical problems in genomic mapping more specifically, semiparametric
copula models in twin research, with D.I. Boomsma, Biological Psychology, VUA,
power of scan statistics for genetic linkage detection; and modelling and
statistics for networks in biology, with D.O. Siegmund, Statistics, Stanford
(USA);
Modelling and statistical analysis of developmental gene networks with J.
Kaandorp, Computer Science, Universiteit van Amsterdam (UvA), and J. Reinitz,
Applied Mathematics and Statistics/Developmental Genetics, Stony Brook
University, USA;
Spatiotemporal modelling and analysis of neuronal activity patterns with A.B. Brussaard, A. van Ooyen, Experimental Neurophysiology, VUA, and J. van Pelt, Neurons and Networks, Netherlands Institute for Brain Research.
Scientific advisor:
EURANDOM aims to be an active player in statistics in
industry. It played a role in the formation of
ENBIS, the European Network for Business and
Industrial Statistics, and is a partner on the EU Thematic Network proENBIS,
which grew out of ENBIS. It has contributed to all the ENBIS annual conferences
and Talía Figarella (PhD student) won the prize for the best presented paper at
the 2003 Conference in Barcelona. Henry Wynn has been founding president of ENBIS
and with colleagues at EURANDOM is active in the proENBIS work packages. Other
international activities include the hosting of Europe's major international
workshops on algebraic statistics (GROSTAT
3, September 1999), respectively optimal design of experiments (mODa, June 2004). Two main scientific themes are:
Algebraic Statistics and Reliability. Related to industrial statistics, but
managed separately from SIM, is the Integrated Batteries project
(IBAT).
Algebraic Statistics
Following success with the application of Gröbner bases to the design of experiments (Wynn and Pistone, Biometrika 1986) the subject of algebraic statistics has been adopted as a main area. The research concentrates on three specific topics:
1. Design of Experiments (DOE)
Industrial practice requires experimental designs for studying the impact of factors on both mean and variance of response factors. Several approaches exist in literature, but a general framework is not yet known. SIM is developing a new algebraic approach including algorithms.2. Contingency tables
Hypothesis testing on contingency tables using asymptotics does not work well in many practical cases. Following the seminal work by Diaconis and Sturmfels, there is a growing interest in using algebraic and geometric methods for MCMC methods, ML estimation and identifiability problems in this area. Within SIM emphasis is on the latter two topics3. Statistical learning
We are exploring possibilities to apply algebraic methods to statistical learning, in particular, the design of kernels for soft sensors.
Reliability
Research on reliability concentrates on two subthemes: Signature Analysis and Software Reliability
1. Signature Analysis
This important area is a critical part of the wider area of "end of life" analysis, which seeks to analyse used products for reuse. This is driven by the new EU WEEE directives to avoid environmentally costly landfill. The art is to be able to quickly detect performance deterioration and predict product life beyond first use, using a blend of designed experiments and signal processing. The Quality and Reliability Engineering group of Aarnout Brombacher with the Department of Technology Management at TU/e is also actively involved in this project.2. Software Reliability
The Statistical Testing and Reliability Estimation of Software Systems (STRESS project).
As a (hardware) extension of the current activities in reliability A. Di Bucchianico has initiated a collaboration on software reliability with the newly founded LaQuSo (Laboratory for Quality Software) of the Department of Mathematics and Computer Science at TU/e. The goal of this collaboration is to develop testing strategies of software that on the one hand incorporate structural knowledge of software using modern computer science models, and on the other hand apply modern statistical methods. A multidisciplinary project proposal was awarded a grant by NWO for 2 PhD students (1 in computer science at LaQuSo, 1 in statistics at EURANDOM). The PhD students started in June 2005. Initial results by A. Di Bucchianico and K. van Hee and J.F. Groote, on release strategies that guarantee error freeness with a certain confidence, have been presented at several software testing conferences.
Members of SIM participate in the EU network programmes proENBIS and ENBIS (European Network for Business and Industrial Statistics), PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning, RESQ (Resources for Quantum Information) and MUSCLE. In addition to these formal cooperations, there are numerous individuallevel cooperations, with scientists throughout Europe and USA.
SIM is a founding member of "QRandom'', an informal international consortium that has organised a sequence of workshops in Aarhus (1999), EURANDOM (2001), Dresden (2002) and Aarhus (2003), on stochastics in quantum information and quantum measurement.
Future workshop(s)
September 2426, 2007
Algoritms in Complex Systems, workshop within the framework of the Network of Excellence (NoE)
PASCAL
October 812, 2007
Workshop "YESI" (Young European Statisticians) 2007
Large
Shape Restricted Inference
5. Former people and past activities
Former Postdocs, Ph.D. students and Research Fellows
Statistical Information and Modelling (previously CSM, AS, CMB)
Name  Postdoc / PhD student / Research Fellow  Period  
1.  Nicola Armstrong  Postdoc  02/2002  04/2004 
2.  Luis Artiles Martinez  Postdoc  01/2002  12/2004 
3.  Isaac Corro Ramos  Postdoc  07/2005  07/2009 
4.  Bojan Basrak  Postdoc  07/2000  07/2003 
5.  Wicher Bergsma  Postdoc  09/2003  09/2005 
6.  Julia Brettschneider  Postdoc  01/2001  08/2001 
7.  Nicolas Brunel  Postdoc  07/2006  12/2006 
8.  Cheikh Diack  Postdoc  11/1998  11/2000 
9.  Sandro Di Bucchianico  Senior Researcher  09/1998  09/2001 
10.  Peter Grünwald  Postdoc  11/1999  11/2001 
11.  Research Fellow  01/2006  01/2007  
12.  Madalin Guta  Postdoc  01/2002  01/2004 
13.  Research Fellow  01/2004  01/2005  
14.  Farida Enikeeva  Postdoc  05/2003  05/2005 
15.  Talía Figarella  PhD student  06/2003  09/2006 
16.  Sonia HernandezAlonso  Postdoc  09/2000  09/2003 
17.  Roxana Ion  Postdoc  06/2001  12/2002 
18.  Alexey Koloydenko  Postdoc  10/2002  10/2005 
19.  Vladimir Kulikov  Postdoc  05/2003  02/2005 
20.  Nadia Lalam  Postdoc  02/2004  11/2006 
21.  Jüri Lember  Postdoc  02/2001  08/2003 
22.  Andries Lenstra  Postdoc  01/2001  01/2003 
23.  Patrick Lindsey  Postdoc  09/2001  09/2003 
24.  Research Fellow  01/2005  01/2006  
25.  Leila Mohammadi  Postdoc  11/2004  11/2006 
26.  Nino Mushkudiani  Postdoc  09/2001  09/2003 
27.  Eva Riccomagno  Postdoc  07/1999  03/2001 
28.  Fabio Rigat  Postdoc  09/2004  09/2006 
29.  Peter van de Ven  PhD student  02/2003  03/2007 
30.  Brandon Whitcher  Postdoc  09/1998  09/2000 
31.  Jian Zhang  Postdoc  06/1999  09/2002 
Visitors
Name  Affiliation  Country  Period 
W. Khamaladze  Victoria University  New Zealand  January 29  February 2, 2006 
J. Kahn  ENS  France  February 6  15, 2006 
S. Kuhnt  Dortmund University  Germany  February 21  24, 2006 
F. D'AlchéBuc  IBISC  France  August 30  31, 2006 November 20, 2006 
M. Viana  University of Illinois at Chicago  USA  December 6  14, 2006 
S. Kuhnt  Dortmund University  Germany  March 2, 2005 
M. Viana  University of Illinois at Chicago  USA  March 1024, 2005 
M. Lupparelli  University of Florence  Italy  April 4  18, 2005 
M. Huskova  Charles University Prague  Czech Republic  June 9  21, 2005 
S. Vidal Puig  Technical University of Valencia  Spain  October 20 December 21, 2005 
E. Khamaladze  Victoria University  New Zealand  December 6  12, 2005 
T. de Bie  Katholieke Universiteit Leuven  Belgium  December 19  20, 2005 
Workshops
Shape Restricted Inference  October 812, 2007 
Algorithms in Complex Systems"  September 2426, 2007 
NEST II Mathematical Methodologies for Operational Risk  April 1618, 2007 
Image Analysis and Inverse Problems  December 1113, 2006 
Statistics for biological networks  January 1618, 2006 
PASCAL  Modelling in Classification and Statistical Learning 
October 35, 2005 
Regional Meeting on Design of Experiments (DOE) and 
June 24, 2005 
Minicourse Symmetry Studies  in cooperation with EIDMA  
March 1418, 2005 
Reports
For information about reports of previous years and/or downloads of abstracts and reports have a look at the EURANDOM reports page.
Last update: September 3 2008
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