Nonparametric and robust multivariate methods with applications
Robustness is a major issue for multivariate analysis as very complicated and contaminated data sets become increasingly common in applied fields. The aim of this research project is to develop new robust multivariate methods that can be used instead of classical normal theory based methods when analyzing such complicated data sets. Methods can be applied for example in the field bioinformatics, chemometrics, signal processing, biology, etc.
Postdoctoral researcher: Jari Miettinen (University of Jyväskylä).
- Taskinen, S. and Warton, D.I. (2013). "Robust tests for one or more allometric lines", Journal of Theoretical Biology, 333, 38-46
- Warton, D.I., Duursma, R.A., Falster, D.S. and Taskinen, S. (2012). "smatr 3 - an R package for estimation and inference about allometric lines", Methods in Ecology and Evolution, 2, 257-259.
- Taskinen, S., Koch, I. and Oja, H. (2012). "Robustifying principal components with spatial sign vectors", Statistics and Probability Letters, 82, 765-774.
- Taskinen, S. and Warton, D.I. (2011). "Robust estimation and inference for allometric line-fitting", Biometrical Journal, 53, 652-672
- Taskinen, S., Sirkiä, S. and Oja, H. (2010). "k-step estimators of shape based on spatial signs and ranks", Journal of Statistical Planning and Inference, 140, 3376-3388.
- Sirkiä, S., Taskinen, S., Oja, H. and Tyler, D. (2009). "Tests and estimates for shape based on spatial signs and ranks", Journal of Nonparametric Statistics, 21, 155-176.
- Kankainen, A., Taskinen, S. and Oja, H. (2007). "Tests of multinormality based on location vectors and scatter matrices", Statistical Methods and Applications, 16, 357-379.