[University of Oulu] [Faculty of Science] [Department of Mathematical Sciences]

Lasse Holmström

Professor (emer.)

Lasse Holmstrm

Contact Information

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

Email: <Lasse.Holmstrom at oulu.fi>
Phone: +358 29 4481739, +358 400 506418
Mobile: +358 50 563 7465


Research interests

Nonparametric function estimation, pattern recognition, data analysis applications in science and technology.

Google Scholar Profile



Research group homepage

The research group on nonparametric estimation of functions and its applications has its homepage at

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

Teaching

Funktioiden estimointi (Function estimation, In Finnish)

Tilastollinen hahmontunnistus (Statistical Pattern Recognition, In Finnish)

Informaatioteoria (Information Theory, In Finnish)


Esitelmä Oulun Tietomaassa 28.11.2012 (in Finnish):

Kalvot pdf-muodossa (katso Acrobat Readerilla "Full Screen" muodossa)

Selected publications

  • L. Pasanen, T. Aakala, and L. Holmström. A scale space approach for estimating the characteristic feature sizes in hierarchical signals. Stat, 7(1):e195, 2018. Available on-line at url https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.195.

  • V. Vuollo and L. Holmström. A scale space approach for exploring structure in spherical data. Computational Statistics & Data Analysis, 125:57 -- 69, 2018. Available on-line at url https://doi.org/10.1016/j.csda.2018.03.014.

  • L. Holmström and L. Pasanen. Statistical scale space methods. International Statistical Review, 85(1):1-30, 2017. The article is accompanied by a discussion. Available on-line at url http://dx.doi.org/10.1111/insr.12155.

  • L. Holmström, K. Karttunen, and J. Klemelä. Estimation of level set trees using adaptive partitions. Computational Statistics, 32:1139-1163, 2017. Available on-line at url http://dx.doi.org/10.1007/s00180-016-0702-2.

  • I. Launonen and L. Holmström. Multivariate posterior singular spectrum analysis. Statistical Methods & Applications, 26(3):361 -- 382, 2017. Available on-line at url http://dx.doi.org/10.1007/s10260-016-0372-9.

  • T. Mäkinen and L. Holmström. Modeling probability density through ultraspherical polynomial transformations. Communications in Statistics - Simulation and Computation, 46(8):5879-5900, 2017. Available on-line at url http://dx.doi.org/10.1080/03610918.2016.1186181.

  • L. Pasanen and L. Holmström. Scale space multiresolution correlation analysis for time series data. Computational Statistics, 32(1):197-218, 2017. Available on-line at url http://dx.doi.org/10.1007/s00180-016-0670-6.

  • L. Ilvonen, L. Holmström, H. Seppä, and S. Veski. A Bayesian multinomial regression model for paleoclimate reconstruction with time uncertainty. Environmetrics, 27(7):409-422, 2016. Available on-line at url http://dx.doi.org/10.1002/env.2393.

  • V. Vuollo, L. Holmström, H. Aarnivala, V. Harila, T. Heikkinen, P. Pirttiniemi, and A. M. Valkama. Analyzing infant head flatness and asymmetry using kernel density estimation of directional surface data from a craniofacial 3D model. Statistics in Medicine, 35(26):4891-4904, 2016. Available on-line at url http://dx.doi.org/10.1002/sim.7032.

  • L. Holmström, L. Ilvonen, H. Seppä, and S. Veski. A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records. The Annals of Applied Statistics, 9(3):1194-1225, 2015. Available on-line at url http://dx.doi.org/10.1214/15-AOAS832, and also at url http://cc.oulu.fi/ llh/preprints/Spattemp.zip.

  • A.E.K. Ojala, I. Launonen, L. Holmström, and M. Tiljander. Effects of solar forcing and North Atlantic oscillation on the climat e of continental Scandinavia during the Holocene. Quaternary Science Reviews, 112(0):153 -- 171, 2015. Available on-line at url http://dx.doi.org/10.1016/j.quascirev.2015.01.021.

  • L. Pasanen and L. Holmström. Bayesian scale space analysis of temporal changes in satellite images. Journal of Applied Statistics, 42(1):50-70, 2015. Available on-line at url http://dx.doi.org/10.1080/02664763.2014.932761.

  • L. Pasanen, L. Holmström, and M. J. Sillanpää. Bayesian LASSO, Scale Space and Decision Making in Association Genetics. PLoS ONE, 10(4):e0120017, 04 2015. Available on-line at url http://dx.doi.org/10.1371/journal.pone.0120017.

  • L. Holmström and I. Launonen. Posterior singular spectrum analysis. Statistical Analysis and Data Mining, 6(5):387-402, 2013. Available on-line at url http://dx.doi.org/10.1002/sam.11195.

  • P. Erästö, L. Holmström, A. Korhola, and J. Weckström. Finding a consensus on credible features among several paleoclimate reconstructions. Annals of Applied Statistics, 6(4):1377-1405, 2012. Available on-line at http://dx.doi.org/10.1214/12-AOAS540, and also at http://cc.oulu.fi/ llh/preprints/Consensus.zip.

  • F. Godtliebsen, L. Holmström, A. Miettinen, P. Eräst ö, D. V. Divine, and N. Koc. Pairwise Scale-Space Comparison of Time Series with Application to Cli mate Research. Journal of Geophysical Research, 117, C03046, 2012. Available on-line at http://dx.doi.org/10.1029/2011JC007546.

  • L. Holmström and L. Pasanen. Bayesian scale space analysis of differences in images. Technometrics, 54(1):16-29, 2012. Available on-line at http://dx.doi.org/10.1080/00401706.2012.648862.

  • 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, 22(4):413 -- 424, 2012.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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

  • 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.

  • 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.

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

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

  • 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.

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

  • 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.

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

  • 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.

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

  • 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.

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

CV and a complete list of publications

Lasse Holmström <Lasse.Holmstrom at oulu.fi> Last modified: Wed Oct 5 15:30:00 2016

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