Bayesian multiscale smoothing for making inferences about features in scatter plots

Panu Erästö, Lasse Holmström

Research output: Contribution to journalArticleScientificpeer-review


A rather common problem of data analysis is to find interesting features, such as local minima, maxima, and trends in a scatterplot. Variance in the data can then be a problem and inferences about features must be made at some selected level of significance. The recently introduced SiZer technique uses a family of nonparametric smooths of the data to uncover features in a whole range of scales. To aid the analysis, a color map is generated that visualizes the inferences made about the significance of the features. The purpose of this article is to present Bayesian versions of SiZer methodology. Both an analytically solvable regression model and a fully Bayesian approach that uses Gibbs sampling are presented. The prior distributions of the smooths are based on a roughness penalty. Simulation based algorithms are proposed for making simultaneous inferences about the features in the data.
Original languageEnglish
Pages (from-to)569-589
Issue number3
Publication statusPublished - 2005
MoE publication typeA1 Journal article-refereed


  • BSiZer
  • Bayesian data analysis
  • Roughness penalty
  • Scale space methods
  • Scatterplot smoothing
  • Simultaneous credible sets
  • SiZer

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