University of Helsinki Department of Mathematics and Statistics
Faculty of Science
Faculty of Social Sciences


Panu Erästö, Ph.D.

Current position

Aalto university, School of business

Research interests

Current interests: Epidemiology, Bayesian statistical modeling, smoothing, visualization, SiZer methodology and especially Bayesian versions of it (see an introduction to SiZer methodology from a non-parametric point of view). Environmental risk analysis.

Other, not that current interests: Statistical learning theory, Support Vector Machines.


Matlab software to produce Bayesian SiZer maps is provided here.

Selected refereed publications related to Bayesian SiZer

  • P. Erästö, L. Holmström. Finding a Consensus on Credible Features Among Several Paleoclimate Reconstructions. Annals of Applied Statistics, 6(4):1377-1405, 2012.

  • F. Godtliebsen, L. Holmström, A. Miettinen, P. Erästö, D. V. Divine, N. Koc. Pairwise Scale-Space Comparison of Time Series with Application to Climate Research. Journal of Geophysical research, doi:10.1029/2011JC007546, 2012.

  • P. Erästö. Studies in Trend Detection of Scatter Plots with Visualization. Doctoral dissertation. 2006. The summary can be found from e-thesis service.

  • 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, online version, DOI:10.1080/10629360600711988, June 22, 2006.

  • P. Erästö and L. Holmström. Selection of Prior Distributions and Multiscale Analysis in Bayesian Temperature Reconstructions Based on Fossil Assemblages. Journal of Paleolimnology, 36(1):69-80, 2006.

  • 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(1):78-86, 2006.

  • 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.

  • 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, P. Erästö, P. Koistinen, J. Weckström, and A. Korhola. Using smoothing to reconstruct the Holocene temperature in Lapland. In Proceedings of INTERFACE 2000, 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.