Lasse Holmstr

Lasse Holmström Jan 30, 2012

CURRICULUM VITAE AND PUBLICATIONS





Name and current address

Lasse Holmström
University of Oulu
Department of Mathematical Sciences
P.O.Box 3000
90014 University of Oulu
Finland

Homepage: http://cc.oulu.fi/~llh

Date and place of birth, marital status

June 27, 1951, Helsinki, Finland
Married, three children

Education

University of Helsinki (1971-1978):

B.S. (Mathematics), 1974
M.S. (Mathematics), 1975
Licentiate in Philosophy (Mathematics), 1977
Clarkson College of Technology, Potsdam, New York, USA (1978 - 1979):
Ph.D. (Mathematics), 1980
Doctoral Thesis: A Study on the Structure of Nuclear Köthe Spaces
Thesis advisor: Professor Ed Dubinsky
Positions held

In Finland

University of Oulu, Department of Mathematical Sciences:
Head of the Department (2006 -)
Professor (2003 -),
Rolf Nevanlinna Institute (University of Helsinki):
Director (1999 - 2000, 2002 - 2003)
Research Division Head (1995 - 2003)
Associate Professor (1994 - 1995)
Senior Fellow (1992 - 1993)
Acting Director (1992)
Research Fellow (1988 - 1989)
Academy of Finland (Research Council for Natural Sciences and Engineering): Senior Scientist (2008)

Academy of Finland (Research Council for Technology): Senior Fellow (1990 - 1992)

Helsinki University of Technology, Laboratory of Information Processing Science: Research Fellow (1984 - 1988)

University of Helsinki, Department of Mathematics:
Assistant (1977 - 1978, 1979 - 1981, 1983 - 1984)
Lecturer (Fall 1980)
Docent of Mathematics (1983 -)
The Institute of Marine Research, Finland: Research Assistant (summers 1974 and 1975)

Abroad

The National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA, Institute for Mathematics Applied to Geosciences (IMAGe): Visiting Senior Scientist (2008)

George Mason University, Fairfax, Virginia, USA, Center for Computational Statistics: Visiting Research Professor, (1997 - 1998)

Rice University, Houston, Texas, USA, Department of Statistics: Visiting Professor (1993)

Vassar College, Poughkeepsie, New York, USA, Department of Mathematics: Visiting Assistant Professor (1982 - 1983)

Clarkson College of Technology, Potsdam, New York, USA, Department of Mathematics and Computer Science: Visiting Assistant Professor (1981 - 1982)

Leader of research projects

Learning systems and their applications (funded by the Academy of Finland, Research Council for Technology, 1990 - 1995).

Self-Organisation and Analogical Modeling using Subsymbolic Computing (funded by the Technology Development Centre, 1989 - 1990, 1991 - 1993).

New Methods in the Analysis of Multidimensional Data (funded by University of Helsinki, 1994 - 1996).

Adaptive Image Analysis, the RNI group (funded by the Technology Development Centre, 1994 - 1995).

Intelligent Processing and Analysis of Images and Speech (funded by the Academy of Finland, Research Council for Science and Technology, 1996 - 1999).

Flexible Function Estimation and Neural Networks (funded by the Academy of Finland, Research Council for Science and Technology, 1999 - 2001).

New Modeling and Data Analysis Methods for Satellite Based Forest Inventory (a research consortium with Rolf Nevanlinna Institute, Finnish Forest Research Institute, and the Laboratory of Space Technology of the Helsinki University of Technology, funded by the Academy of Finland ANTARES Research Programme, 2001 - 2004).

Measuring the Environment: Analyzing Data from Fossils to Forests (funded by the Academy of Finland, Research Council for Science and Technology, 2003 - 2006).

Climate variability in NW Europe during the last 4000 years and its ecological consequences (CLIM-ECO) - Mathematical theory and predictive models for temporal dynamics (funded by the Academy of Finland, Research Council for Biosciences and Environment, 2008 -).

Doctoral and licentiate's theses directed

Doctor:
Ari Hämäläinen, University of Jyväskylä, 1995
Petri Koistinen, Helsinki University of Technology, 1996
Jussi Klemelä, University of Helsinki, 1997
Fabian Hoti, University of Helsinki, 2004
Panu Erästö, University of Helsinki, 2006
Licentiate:
Timo Laakko, Helsinki University of Technology, 1987
Ari Hämäläinen, University of Jyväskylä, 1992
Jussi Klemelä, University of Helsinki, 1992
Fabian Hoti, University of Helsinki, 2001
Panu Erästö, University of Helsinki, 2001
Heikki Kokkonen, University of Oulu, 2007
Juna-Matti Tirilä, University of Oulu 2010

Editorial Work

Associate Editor of Scandinavian Journal of Statistics, 2004 - 2010

Referee for several leading international journals in my field, such as Journal of the American Statistical Society, Technometrics, Computational Statistics and Data Analysis, Sankhya, IEEE Transactions on Signal Processing, Pattern Recognition Letters, IEEE Transactions on Neural Networks, Statistical Analysis and Data Mining

Other academic activities

Doctoral thesis defense opponent:
Jukka Heikkonen, Lappeenranta University of Technology, 1994
Doctoral thesis referee:
Jari Kangas, Helsinki University of Technology, 1994
Samuel Kaski, Helsinki University of Technology, 1996
Ilmari Juutilainen, University of Oulu, 2006
Miika Toivanen, Aalto University, 2010
Licentiate's thesis referee:
Jukka Ranta, University of Helsinki, 1996
Tommi Vuorenmaa, University of Helsinki, 2004
Jukka Kemppainen, University of Oulu, 2004
Professorship referee:
Jouko Lampinen, Helsinki University of Technology, 2000
Jouko Lampinen, Helsinki University of Technology, 2005
Docentship referee:
Seppo Pohjolainen, University of Jyväskylä, 1996
Jari Kangas, Helsinki University of Technology, 1996
Jari Kangas, Tampere University of Technology, 1997
Aki Vehtari, University of Helsinki, 2006

Graduate School Board Member

The Finnish Graduate School in Stochastics, 1998 - 2006
The Finnish Graduate School in Stochastics and Statistics, 2006 -
School of Statistical Information, Inference, and Data Analysis, 2002 - 2006
Graduate School of Remote Sensing, 2002 - 2006
Graduate School in Computational Methods of Information Technology, 2001 - 2009

Other Academic Positions of Trust

Member of the management group of the Finnish International Visitor Program in Mathematics, 2001 -

Trustee of the Research Foundation of Rolf Nevanlinna Institute, 1999 -

Member of the Board of Rolf Nevanlinna Institute, 1993 - 2003

Member of the Council of the Faculty of Science, University of Oulu, 2005

Member of the Rolf Nevanlinna Institute Doctoral Thesis Prize Committee 2001 and 2009
 
Congress Committees

1989 Nordic Symposium on Neural Computing, Organizing Committee
1991 International Conference on Artificial Neural Networks, Program Committee
1996 International Conference on Artificial Neural Networks, Program Committee
2002 The 13th European Conference on Machine Learning (ECML'02), Program Committee
2008 The European Workshop on Intelligent Computational Methods and Applied Mathematics (ICMAM 2008), Program Committee

Professional societies

Member of:
American Statistical Association
Finnish Mathematical Society
Institute of Mathematical Statistics
Pattern Recognition Society of Finland

Summary of past and current research work

During the first four postdoctoral years my research work focused on functional analysis. Publications [1,2,3,4,5,6,90] deal with topological vector spaces. In particular, the structure of nuclear Fréchet spaces is studied.

Work on applied mathematics and information technology began in 1984 as I joined the geometric modeling and computer aided design research group at the Helsinki University of Technology. Research papers [7,8,9,10,91,92,94,111] deal with solid modeling, CAD systems and computer graphics. The work from this period includes results on mathematical questions of surface representation in geometric computations and surface visualization and it also describes software development for a prototype solid modeling system.

From 1988 to 2003 I worked at the Rolf Nevanlinna Institute, a research institute of mathematics, computer science, and statistics in the University of Helsinki. The research activity of my former group at the Rolf Nevanlinna Institute, the Division of Mathematical Methods of Information Technology, is described in more detail at http://www.rni.helsinki.fi/research/info. My own research centered in particular on computationally intensive statistical methods, neural computing, pattern recognition, together with extensive cross-disciplinary applied work in various areas of science and technology. Theoretical work includes e.g. [14,15,16,17,23,29,31,36,33,38,40,41,43] and some applications are described in [11,12,13,20,21,25,27,28,32,33,93,103,107,34,35,38,39,45,110].

My present research at University of Oulu focuses on the development of flexible computational methods of statistical data analysis with applications to problems in science and technology. Of special interest is the scale-space approach to smoothing [40,43,47,46]. The most recen work centers on the use of Bayesian scale-methodology in image analysis [44,49,83].

In application projects, I have collaborated with the brain research group of the Low Temperature Laboratory of the Helsinki University of Technology [12,95] and the robotics group of the Institute of Industrial Automation of the Helsinki University University of Technology [11,13,96,98]. Through my group the Rolf Nevanlinna Institute was a partner in a European Community Esprit Basic Research project that studied self-organization and analogical modeling in the context of vision based robot control [100,101,103]. During my visit to Rice University in 1993, scientific collaboration was started with researchers from both the Statistics and the Physics Departments of Rice University [19,21,22,28,35,102,104,107,114,35]. The pattern recognition technique I helped to develop contributed to the discovery of the top quark in the mid 90's. Recognition of handwritten digits was studied in a joint project with the Laboratory of Computer and Information Science of the Helsinki University of Technology [24,27,105]. Signal detection in the context of multi-user mobile communication was studied in cooperation with the NOKIA Research Center in [20,25].

A joint project on remote sensing with the Finnish Forest Research Institute and the Laboratory of Space Technology of the Helsinki University of Technology [109,45,110] focused on the development of new methods for satellite based forest inventory. Collaboration with the researchers of the National Public Health Institute involved data-analysis of neuropsychological testing of schizophrenia with the aim of discovering the genetic basis of the disease [39]. On-going cross-disciplinary research includes joint work with the ECRU unit of the Department of Biological and Environmental Sciences at the University of Helsinki on Holocene temperature reconstruction [32,33,61,62,108,62,59,40,43,41,,42,52]. Collaboration with the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, USA, started during my visit there in 2008. Currently our joint work focuses on scale space analyses of numerical climate prediction models outputs [53,84].

Publications

Appeared and Submitted Refereed Publications

[1]
L. Holmström. On stable D1 and D2 spaces. Archiv der Mathematik, 36:546-553, 1981.

[2]
L. Holmström. Universal classes of nuclear Köthe spaces with a continuous norm. Journal of Functional Analysis, 48(1):12-19, 1982.

[3]
L. Holmström. A note on countably normed nuclear spaces. Proceedings of the American Mathematical Society, 89(3):453-456, 1983.

[4]
L. Holmström. Superspaces of (s) with basis. Studia Mathematica, 75:139-152, 1983.

[5]
E. Dubinsky and L. Holmström. Nuclear Fréchet spaces with locally round finite dimensional decompositions. Monatshefte fur Mathematik, 97:257-275, 1984.

[6]
L. Holmström. Superspaces of (s) with strong finite dimensional decomposition. Archiv der Mathematik, 42:58-66, 1984.

[7]
L. Holmström. Piecewise quadric blending of implicitly defined surfaces. Computer Aided Geometric Desig, 4:171-189, 1987.

[8]
L. Holmström and T. Laakko. A rounding facility for solid modeling of mechanical parts. Computer Aided Design, 20(10):605-614, 1988.

[9]
L. Holmström and T. Laakko. A blending facility for solid modeling of mechanical parts. In F. Kimura and A. Rolstadas, editors, Computer Applications in Production Engineering CAPE '89, pages 309-316. Elsevier Science Publishers B.V., 1989.

[10]
L. Holmström, M. Mäntylä, P. Rekola, and T. Laakko. Ray tracing of boundary models with implicit blend surfaces. In W. Strasser and H-P Seidel, editors, Theory and Practice of Geometric Modeling, pages 253-271. Springer-Verlag, 1989.

[11]
Jarmo T. Alander, Antti Autere, Lasse Holmström, Peter Holmström, Ari Hämäläinen, and Juha Tuominen. Surface type recognition by a hair sensor using neural network methods. In Erdal Arikan, editor, Proceedings of the 1990 Bilkent International Conference on New Trends in Communication, Control, and Signal Processing (BILCON), volume II, pages 1757-1764, Ankara, 2. - 5. July 1990.

[12]
L. Holmström, P. Koistinen, and R. J. Ilmoniemi. Classification of unaveraged evoked cortical magnetic fields. In Proc. IJCNN-90-WASH DC, pages II: 359-362. Lawrence Erlbaum Associates, 1990.

[13]
J. T. Alander, M. Frisk, L. Holmström, A. Hämäläinen, and J. Tuominen. Process error detection using self-organizing feature maps. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, volume 2, pages 1229-1232. Elsevier Science Publishers B.V. (North-Holland), 1991.

[14]
L. Holmström and J. Klemelä. Asymptotic bounds for the expected L1 error of a multivariate kernel density estimator. Journal of Multivariate Analysis, 42(2):245-266, 1992.

[15]
L. Holmström and P. Koistinen. Using additive noise in back-propagation training. IEEE Transactions on Neural Networks, 3(1):24-38, January 1992.

[16]
P. Koistinen and L. Holmström. Kernel regression and backpropagation training with noise. In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors, Advances in Neural Information Processing Systems 4, pages 1033-1039, San Mateo, CA, 1992. Morgan Kaufmann Publishers.

[17]
L. Holmström and A. Hämäläinen. The self-organizing reduced kernel density estimator. In Proceedings of the 1993 IEEE International Conference on Neural Networks, San Francisco, California, March 28 - April 1, volume 1, pages 417-421, 1993.

[18]
L. Holmström and T. Kohonen. Neural networks. In E. Hyvönen, I. Karanta, and M. Syrjänen, editors, Encyclopaedia of Artificial Intelligence, pages 85-98. Gaudeamus Oy, 1993. In Finnish.

[19]
L. Holmström. Neural networks vs. statistics: A comparison using high-energy physics data. In A. B. Bulsari and S. Kallio, editors, Engineering Applications of Artificial Neural Networks. Proceedings of the International Conference EANN'95, Otaniemi, 21-23 August 1995, Finland, pages 441-444, 1995.

[20]
L. Holmström, A. Hottinen, and A. Hämäläinen. Using a self-organizing kernel density estimator for CDMA communications. In A. B. Bulsari and S. Kallio, editors, Engineering Applications of Artificial Neural Networks. Proceedings of the International Conference EANN'95, Otaniemi, 21-23 August 1995, Finland, pages 445-448, 1995.

[21]
L. Holmström, S.R. Sain, and H.E. Miettinen. A new multivariate technique for top quark search. Computer Physics Communications, 88:195-210, 1995.

[22]
H.E. Miettinen, L. Holmström, and S.R. Sain. Top quark search with probability density estimates and neural networks. In B. Denby and D. Perret-Gallix, editors, New Computing Techniques in Physics Research IV, pages 473-478, Singapore, 1995. World Scientific.

[23]
A. Hämäläinen and L. Holmström. Complexity reduction in probabilistic neural networks. In C. von der Malsburg, W. von Seelen, J.C.Vorbrüggen, and B. Sendhoff, editors, Artificial Neural Networks-ICANN' 96, Proceedings of the 1996 International Conference, Bochum, Germany, pages 65-70, July 1996. Lecture Notes in Computer Science 1112, Springer.

[24]
L. Holmström, P. Koistinen, J. Laaksonen, and E. Oja. Neural network and statistical perspectives of classification. In Proceedings of the 13th International Conference on Pattern Recognition, ICPR-96, Vienna, pages IV: 286-290, Los Alamitos, CA, 1996. IEEE Computer Society Press.

[25]
A. Hottinen and L. Holmström. Projection pursuit for CDMA communications. In Proceedings of the 30th Annual Conference on Information Sciences and Systems (CISS'96), pages 101-106, New Jersey, March 1996.

[26]
L. Holmström. The error and the computational complexity of a multivariate binned kernel density estimator. In D.W. Scott, editor, Computing Science and Statistics, 29(1), pages 519-528. Interface Foundation of North America, Inc., Fairfax Station, VA 22039-7460, 1997.

[27]
L. Holmström, P. Koistinen, J. Laaksonen, and E. Oja. Neural and statistical classifiers-taxonomy and two case studies. IEEE Transactions on Neural Networks, 8(1):5-17, 1997.

[28]
L. Holmström and S.R. Sain. Multivariate discrimination methods for top quark analysis. Technometrics, 39(1):91-99, February 1997.

[29]
L. Holmström and F. Hoti. Radial basis function classification as computationally efficient kernel regression. In IJCNN '98, Proceedings of the 1998 IEEE International Joint Conference on Neural Networks, Anchorage, Alaska, May 4-9, pages 1305-1310, 1998.

[30]
F. Hoti and L. Holmström. Reduced Kernel Regression for Fast Classification. In Leif Arkeryd, Jöran Berg, Philip Brenner, and Rolf Pettersson, editors, Progress in Industrial Mathematics at ECMI 98, pages 405-412. B. G. Teubner Stuttgart · Leipzig, 1999.

[31]
L. Holmström. The accuracy and the computational complexity of a multivariate binned kernel density estimator. Journal of Multivariate Analysis, 72(2):264-309, 2000.

[32]
A. Korhola, J. Weckström, L. Holmström, and P. Erästö. A quantitative Holocene climatic record from diatoms in northern Fennoscandia. Quaternary Research, 54:284-294, 2000.

[33]
L. Holmström and P. Erästö. Making inferences about past environmental change using smoothing in multiple time scales. Computational Statistics & Data Analysis, 41(2):289-309, 2002.

[34]
F.J. Hoti, M.J. Sillanpää, and L. Holmström. A note on estimating the posterior density of a qualitative trait locus from a Markov chain monte carlo sample. Genetic Epidemiology, 22:369-376, 2002.

[35]
B. Knuteson, H.E. Miettinen, and L. Holmström. aPDE: A new multivariate technique for parameter estimation. Computer Physics Communications, 145(3):351-356, 2002.

[36]
F. Hoti and L. Holmström. On the estimation error in binned local linear regression. Journal of Nonparametric Statistics, 15(4-5):625-642, 2003.

[37]
F. Hoti and L. Holmström. Application of semiparametric density estimation to classification. In Proceedings of the 17th International Conference on Pattern Recognition, ICPR2004, Volume 3, Session 2P.We-i (Classification), Cambridge, United Kingdom, 2004. IEEE Computer Society Press, Los Alamitos, CA.

[38]
F. Hoti and L. Holmström. A semiparametric density estimation approach to pattern classification. Pattern Recognition, 37(3):409-419, 2004.

[39]
F. Hoti, A. Tuulio-Henriksson, J. Haukka, T. Partonen, L. Holmström, and J. Lönnqvist. Family-based clusters of cognitive test performance in familial schizophrenia. BMC Psychiatry, http://www.biomedcentral.com/1471-244X/4/20, 4:20, 2004.

[40]
P. Erästö and L. Holmström. Bayesian multiscale smoothing for making inferences about features in scatter plots. Journal of Computational and Graphical Statistics, 14(3):569-589, 2005.

[41]
P. Erästö and L. Holmström. Prior selection and multiscale analysis in Bayesian temperature reconstruction based on species assemblages. Journal of Paleolimnology, 36(1):69-80, 2006.

[42]
J. Weckström, A. Korhola, P. Erästö, and L. Holmström. Temperature Patterns over the Past Eight Centuries in Northern Fennoscandia Inferred from Sedimentary Diatoms. Quaternary Research, 66:78-86, 2006.

[43]
P. Erästö and L. Holmström. Bayesian analysis of features in a scatter plot with dependent observations and errors in predictors. Journal of Statistical Computation and Simulation, 77(5):421-431, 2007.

[44]
L. Holmström and L. Pasanen. Bayesian analysis of image differences in multiple scales. In Matti Niskanen and Janne Heikkilä, editors, Proceedings, Finnish Signal Processing Symposium 2007, August 30, Oulu, Finland. University of Oulu, Department of Electrical and Information Engineering, 2007. CD-ROM, ISBN 978-951-42-8546-2.

[45]
P. Koistinen, L. Holmström, and E. Tomppo. Smoothing methodology for predicting regional averages in multi-source forest inventory. Remote Sensing of Environment, 112(3):862-871, 2008.

[46]
L. Holmström. BSiZer. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5):526-534, 2010. Available on-line at http://dx.doi.org/10.1002/wics.115.

[47]
L. Holmström. Scale space methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(2):150-159, 2010. Available on-line at http://dx.doi.org/10.1002/wics.79.

[48]
L. Holmström and P. Koistinen. Pattern recognition. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4):404-413, 2010. Available on-line at http://dx.doi.org/10.1002/wics.99.

[49]
L. Holmström and L. Pasanen. Bayesian scale space analysis of differences in images. http://cc.oulu.fi/~llh/preprints/iBSiZer.pdf, 2010. Technometrics, to appear.

[50]
P. Erästö, L. Holmström, A. Korhola, and J. Weckström. Finding a consensus on credible features among several paleoclimate reconstructions. http://cc.oulu.fi/~llh/preprints/consensus.pdf, 2011. The Annals of Applied Statistics, to appear.

[51]
F. Godtliebsen, L. Holmström, A. Miettinen, P. Erästö, D. V. Divine, and N. Koc. Pairwise Scale-Space Comparison of Time Series with Application to Climate Research. Submitted for publications, 2011.

[52]
L. Holmström. Discussion of: A statistical analysis of multiple temperature proxies: are reconstructions of surface temperatures over the last 1000 years reliable? by B. B. McShane and A. J. Wyner. The Annals of Applied Statistics, 5(1):71 - 75, 2011. Available on-line at http://dx.doi.org/10.1214/10-AOAS398H.

[53]
L. Holmström, L. Pasanen, R. Furrer, and S. R. Sain. Scale space multiresolution analysis of random signals. Computational Statistics & Data Analysis, 55(10):2840 - 2855, 2011. Available on-line at http://dx.doi.org/10.1016/j.csda.2011.04.011.

[54]
S. Salonen, L. Ilvonen, H. Seppä, L. Holmström, R. J. Telford, A. Gaidamavicius, M. Stancikaite, and D. Subetto. Comparing different calibration methods (WA/WA-PLS regression and Bayesian modelling) and different-sized calibration sets in pollen-based quantitative climate reconstruction. The Holocene, to appear, 2011.

[55]
L. Pasanen and L. Holmström. Bayesian scale space analysis of temporal changes in Landsat ETM+ satellite images. Submitted for publication, 2012.

Non-Refereed Publications in Conference Proceedings and Collections

[56]
L. Holmström and J. Klemelä. Choosing an L1 optimal smoothing parameter in kernel density estimation. In Proceedings of the Workshop on Symbolic and Numeric Computation, Helsinki May 30 - 31, Computing Centre, University of Helsinki, Research Reports 16, 1991.

[57]
L. Holmström and S. Sain. Using multivariate discrimination in top quark search. In American Statistical Association, 1995 Proceedings of the Statistical Computing Section, Orlando, Florida, USA, August 13 - 17, pages 102-107, 1995.

[58]
L. Holmström, F. Hoti, and P. Koistinen. Experiments in polychotomous classification. In Bulletin of the International Statistical Institute, ISI 99, the 52nd Session of the International Statistical Institute, August 10 - 18, 1999, Helsinki, Finland, Contributed Papers, Tome LVIII, Three Books, Book 2, page 41, 1999.

[59]
L. Holmström, P. Erästö, P. Koistinen, J. Weckström, and A. Korhola. Using smoothing to reconstruct the Holocene temperature in Lapland. In E. Wegman and Y. Martinez, editors, Computing Science and Statistics, 32. Modeling the Earth's Systems: Physical to Infrastructural. Proceedings of the 32nd Symposium on the Interface, pages 425-437, Fairfax Station, VA, USA, 2000. Interface Foundation of North America, Inc. Invited paper.

[60]
L. Holmström, P. Koistinen, F. Hoti, and P. Erästö. Classification of Complex Data. In Year 2000, 5th World Congress of the Bernoulli Society for Mathematical Statistics and Probability and 63rd Meeting of the Institute of Mathematical Statistics. Progrman, Abstracts and Directory of Participants, page 76, Guanajuato, Mexico, 2000. Invited paper.

[61]
P. Erästö, L. Holmström, A. Korhola, and J. Weckström. Sizer - a tool for inferring significant features in environmental reconstructions. In Past Climate Variability Through Europe and Africa, An International Conference. Abstracts, page 79, Centre des Congrès, Aix-en-Provence, France, August 27-31, 2001.

[62]
A. Korhola, J. Weckström, K. Vasko, H. T. Toivonen, L. Holmström, and P. Erästö. Holocene climate records from aquatic organisms in Finnish Lapland: Comparison of various models and proxies. In Mikko Lahti, Linda Talve, Sakari Tuhkanen, and Jukka Käyhkö, editors, CLIC, Climate change variability in northern Europe, Climate change symposium, Programme and abstracts, page 63, Turku/Åbo, Finland, June 6-8th, 2001.

[63]
M. Sillanpää, F. Hoti, and L. Holmström. Estimating the posterior density of a quantitative trait locus from a Markov chain Monte Carlo sample. In 7th Quantitative Trait Locus Mapping and Marker-Assisted Selection Workshop, page 41, Universidad Politécnica de Valencia, October 19-20th, 2001.

[64]
L. Holmström and P. Koistinen. Using additive noise in back-propagation training. In Jukka Iivarinen, Samuel Kaski, and Erkki Oja, editors, Neljännesvuosisata Hatutusta: Hahmontunnistustutkimus Suomessa 1977 -2002, pages 285 - 301. Suomen hahmontunnistustutkimuksen seura ry, Pattern Recognition Society of Finland, 2002. Reprint of [15].

[65]
L. Holmström, P. Koistinen, J. Sarvas, E. Tomppo, and L. Zurk. A polarimetric scattering model and a new approach to the estimation of forest parameters. In Jouni Jussila, Tuomo Nygrén, and Väinö Kelhä, editors, The IX Meeting of Finnish National COSPAR and ANTARES Fall Seminar 2002, page 38, Oulu, Finland, 2002.

[66]
P. Erästö and L. Holmström. Bayesian SiZer - a tool for inferring significant features in environmental reconstructions. In 9th International Paleolimnology Symposium, Abstracts Volume, Espoo, Finland, 2003.

[67]
P. Erästö and L. Holmström. Bayesian SiZer - a tool for parametric data analysis of scatter plots. In Bulletin of the International Statistical Institute 54th Session, Proceedings (CD-ROM), August 13 - 20, Berlin, Germany, 2003.

[68]
P. Erästö and L. Holmström. Bayesian SiZer - a tool for parametric data analysis of scatter plots. In B. Fournier, R. Furrer, T. Gsponer, and E.-M. Restle, editors, Proceedings of the 13th European Young Statisticians Meeting (EYSM'03), Ovronnaz, Switzerland, September 21-26, 2003, 2003.

[69]
L. Holmström. Discussion of the invited paper meeting 19: Numerical methods in statistics including iterative methods for non-linear problems. In Bulletin of the International Statistical Institute 54th Session, Proceedings (CD-ROM), August 13 - 20, Berlin, Germany, 2003. Invited paper.

[70]
F. Hoti and L. Holmström. A semiparametric approach to statistical pattern recognition. In Bulletin of the International Statistical Institute 54th Session, Proceedings (CD-ROM), August 13 - 20, Berlin, Germany, 2003.

[71]
P. Erästö and L. Holmström. Bayesian analysis of trends in a two-dimensional scatter plot. In In 20th Nordic Conference on Mathematical Statistics. Abstracts volume, Jyväskylä, Finland, 2004.

[72]
P. Erästö and L. Holmström. Bayesian analysis of trends in a two-dimensional scatter plot. In COMPSTAT'04 - 16th Symposium of IASC on Computational Statistics. Book of abstracts, page 254, Prague, Czech Republic, 2004. Czech Statistical Society.

[73]
P. Erästö and L. Holmström. BSiZer for making Bayesian inferences about features in scatter plots. In 6th World Congress of the Bernoulli Society for Mathematical Statistics and Probability and 67th Annual Meeting of the Institute of Mathematical Statistics. Progrmamme, Abstracts and Directory of Participants, pages 115 - 116, Barcelona, Spain, 2004.

[74]
L. Holmström and P. Erästö. A Bayesian approach for making inferences about features in scatter plots. In 25th European Meeting of Statisticians, Final Programme and Abstracts, pages O-354, Oslo, Norway, 2005.

[75]
P. Koistinen, L. Holmström, and E. Tomppo. Using local linear smoothing for predicting regional averages in multi-source forest inventory. In C. Kleinn, J. Nieschulze, and B. Sloboda, editors, Remote Sensing and Geographical Information Systems for Environmental Studies: Applications in Forestry, Schriften aus der Forstlichen Fakultät der Universität Göttingen und der Niedersächsischen Forstlichen Versuchsanstalt, Band 138, pages 275-283, 2005.

[76]
A. Korhola, J. Weckström, P. Erästö, and L. Holmström. A 800-year record of summer temperature in northern fennoscandia inferred from sedimentary diatoms. In HOLIVAR 2006. Natural Climate Variability and Global Warming. Final Open Science Meeting. Abstract Volume: 119, University College London, UK, 2006.

[77]
A. Korhola, J. Weckström, L. Holmström, and P. Erästö. Reconstructing climate from palaeolimnological archives using multiple proxy indicators and sites simultaneously. In 10th International Paleolimnology Symposium. Abstract Volume: 94, Duluth, MN, USA, 2006.

[78]
L. Holmström. Nonlinear Dimensionality Reduction by John A. Lee, Michel Verleysen. International Statistical Review, 76(2):308-309, 2008.

[79]
L. Holmström, P. Erästö, J. Weckström, M. Nyman, and A. Korhola. A Bayesian Reconstruction of Holocene Temperature Variation in Northern Fennoscandia. In 2008 Joint Statistical Meetings, Abstract Book, page 256, Denver, Colorado, USA, 2008.

[80]
L. Holmström and L. Pasanen. Bayesian multiscale analysis of differences in noisy images. In 7th World Congress in Probability and Statistics. Programme, Abstracts and Directory of Participants, page 115, Singapore, 2008.

[81]
L. Holmström and L. Pasanen. Bayesian multiscale analysis of differences in noisy images. In International Society for Bayesian Analysis, 9th World Meeting. Abstracts booklet, pages 139-140, Hamilton Island, Australia, 2008.

[82]
A. Korhola, M. Väliranta, L. Holmström, H. Seppä, E.-S Tuittila, J. Laine, and J. Alm. Last-millennium moisture and temperature variations in northern Europe based on proxy data. In Geophysical Research Abstracts, Vol. 10, EGU2008-A-03940, 2008, SRef-ID: 1607-7962/gra/EGU2008-A-03940, European Geosciences Union General Assembly 2008, Vienna, Austria, 2008.

[83]
L. Pasanen and L. Holmström. Bayesian Scale Space Analysis of Image Differences. In Proceedings of the 2008 Joint Statistical Meetings, Section on Statistical Computing, pages 1786-1793, Denver, Colorado, USA, 2008.

[84]
L. Pasanen, L. Holmström, Reinhart Furrer, and S. R. Sain. Bayesian multiscale analysis of image differences. In Statistical Issues in Monitoring the Environment, A Workshop on Environmetrics, Section on Statistics and Environment of the American Statistical Association and the National Center for Atmospheric Research, Boulder, Colorado, USA, 2008.

[85]
L. Holmström. Bayesian scale space smoothing with application to climate reconstruction and prediction. Invited talk. In Program & Abstract Book, The 1st Insititute of Mathematical Statistics Asia Pacific Rim Meeting, pages 132-133, Seoul, Korea, 2009.

[86]
L. Holmström and L. Pasanen. Bayesian scale space analysis with application to remote sensing and climate modeling. In Book of Abstacts, TIES 2009 - the 20th Annual Conference of the International Environmetrics Society and GRASPA Conference, page 53, Bologna, Italy, 2009.

[87]
L. Holmström. Analyzing past climate change using Bayesian scale space smoothing. Invited talk. In 73rd Annual Meeting of the Institute of Mathematical Statistics. Abstracts, http://www.ims-gothenburg.com/abstracts/index.htm, Gothenburg, Sweden, 2010.

[88]
L. Holmström. Scale space methods in climate research. Invited talk. In Conference on Nonparametric Statistics and Statistical Learning, The Blackwell and Pfahl Conference Center, the Ohio State University, USA, 2010.

[89]
J. S. Salonen, L. Ilvonen, H. Seppä, and L. Holmström. Quantitative Paleoclimate Reconstructions from Arctic Russia - Evaluating the Effect of Calibration Method Choice (WA/WA-PLS Regression and Bayesian Modeling) and Calibration Set Size. XVIII INQUA Congress, Bern, Switzerland, 2011.

Technical Reports

[90]
L. Holmström. Infinite type power series spaces and quotient maps. In L. Holmström, editor, Notes on Functional Analysis (Dedicated to Klaus Vala on his 50th birthday), pages 27-33, Reports of the Department of Mathematics, University of Helsinki, June 1980.

[91]
L. Holmström, T. Laakko, M. Mäntylä, and M. Ranta. HutDesign Version 1.0 Maintenance Manual. Report HTKK-TKO-C21, Laboratory of Information Processing Science, Helsinki University of Technology, 1987.

[92]
L. Holmström, T. Laakko, M. Mäntylä, and M. Ranta. HutDesign Version 1.0 User's Guide. Report HTKK-TKO-C20, Laboratory of Information Processing Science, Helsinki University of Technology, 1987.

[93]
L. Holmström, P. Koistinen, and J. Sarvas. Using pattern recognition and neural networks techniques in the design of a metal detector gate. Internal Reports C5, Rolf Nevanlinna Institute, 1988.

[94]
L. Holmström, T. Laakko, M. Mäntylä, M. Ranta, and P. Rekola. Geometric WorkBench Version 1.0 Programmers Guide. Report HTKK-TKO-C29, Laboratory of Information Processing Science, Helsinki University of Technology, 1988.

[95]
L. Holmström, P. Koistinen, and R. J. Ilmoniemi. Classification of unaveraged evoked cortical magnetic fields. Research Reports A1, Rolf Nevanlinna Institute, September 1989.

[96]
A. Autere, J. T. Alander, L. Holmström, P. Holmström, A. Hämäläinen, and J. Tuominen. Surface type recognition by a hair sensor. Research Reports A2, Rolf Nevanlinna Institute, University of Helsinki, 1990.

[97]
L. Holmström and P. Koistinen. Using additive noise in back-propagation training. Research Reports A3, Rolf Nevanlinna Institute, December 1990.

[98]
J. T. Alander, M. Frisk, L. Holmström, A. Hämäläinen, and J. Tuominen. Process error detection using self-organizing feature maps. Research Reports A5, Rolf Nevanlinna Institute, University of Helsinki, 1991.

[99]
L. Holmström and J. Klemelä. An asymptotic upper bound for the expected L1 error of a multivariate kernel density estimator. Research Reports A6, Rolf Nevanlinna Institute, 1991.

[100]
P. Koistinen and L. Holmström. A framework for the design of feature detectors by self-organization: Final report of subtask 1.1. Technical report, Rolf Nevanlinna Institute, 1992. An internal report of the Esprit basic research project ``Selforganisation and analogical Modeling Using Subsymbolic Computation''.

[101]
P. Koistinen and L. Holmström. A framework for the design of feature detectors by self-organization: Preliminary report of subtask 1.1. Technical report, Rolf Nevanlinna Institute, 1992. An internal report of the Esprit basic research project ``Selforganisation and analogical Modeling Using Subsymbolic Computation''.

[102]
L. Holmström and S. Sain. Searching for the top quark using multivariate density estimates. Technical Report No 93-3, Department of Statistics, Rice University, Houston Texas 77251-1892, December 1993.

[103]
P. Koistinen and L. Holmström. A framework for the design of feature detectors by self-organization. Research Reports A10, Rolf Nevanlinna Institute, 1993.

[104]
H.E. Miettinen, R. Ou, L. Holmström, and S. Sain. Searching for top with neural nets II. NN versus probability density estimation. DØ Note 1931, Department of Physics, Rice University, Houston, Texas 77251-1892, November 2 1993.

[105]
L. Holmström, P. Koistinen, J. Laaksonen, and E. Oja. Comparison of neural and statistical classifiers-theory and practice. Research Reports A13, Rolf Nevanlinna Institute, 1996.

[106]
L. Holmström. The error and the computational complexity of a multivariate binned kernel density estimator. Research Reports A17, Rolf Nevanlinna Institute, July 1997.

[107]
B. Knuteson, H. Miettinen, and L. Holmström. Mass Analysis and Parameter Estimation with PDE. DØ Note 3396, Lawrence Berkeley National Laboratory, Berkeley, California, September 8, 1998.

[108]
L. Holmström and Panu Erästö. Using the SiZer method in Holocene temperature reconstruction. Research Reports A36, Rolf Nevanlinna Institute, August 2001.

[109]
L.M. Zurk, P. Koistinen, J. Sarvas, and L. Holmström. Electromagnetic scattering model for forest remote sensing. Research Reports A38, Rolf Nevanlinna Institute, 2002.

[110]
J. Sarvas, J. Praks, L. M. Zurk, P. Koistinen, M. Hallikainen, J. Pulliainen, and L. Holmström. A polarimetric forest scattering model and its validation. An unpublished manuscript, 2004.

Manuscripts

[111]
L. Holmström. A polyhedron evaluator for solid modeling of mechanical parts. Manuscript, Laboratory of Information Processing Science, Helsinki University of Technology, 1988.

[112]
L. Holmström and P. Koistinen. Robot error detection through learning-a sketch of a neural network approach. Manuscript, Rolf Nevanlinna Institute, 1988.

[113]
L. Holmström. Statistical pattern recognition (in Finnish). Lecture notes, Rolf Nevanlinna Institute, 1994.

[114]
L. Holmström. Mass analysis and regression. Manuscript, Rolf Nevanlinna Institute, 1995.

[115]
L. Holmström. Estimation of functions (in Finnish). Lecture notes, Rolf Nevanlinna Institute, 2002.

Edited Publications

[116]
L. Holmström (editor). Notes on functional analysis (dedicated to professor klaus vala on his 50th birthday). Reports of the Department of Mathematics, University of Helsinki, June 1980.

[117]
L. Holmström (editor). Notes on functional analysis II (dedicated to professor klaus vala on his 50th birthday). Reports of the Department of Mathematics, University of Helsinki, November 1980.

Articles in Non-Scientific Publications

[118]
L. Holmström and J. Pihko. Mersenne and Cray (in Finnish). Korkeakoulujen ATK-uutiset, (2):50-51, 1984.

[119]
L. Holmström. Neural net work at Rolf Nevanlinna Institute. ECMI News letter, (6):23-24, October 1989. Helsinki University Press.



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