6 papers · 1 filter
Model-based sparse mixed-type PCA
Lauri Heinonen, Joni Virta
This work presents a new method for principal component analysis (PCA) of a mixed-type data consisting of continuous, binary, integer-valued and positive continuous variables. The…
Data anonymization in the presence of outliers via invariant coordinate selection
Katariina Perkonoja, Joni Virta
Protecting confidential data while preserving utility is particularly challenging when data sets contain outlying observations. Existing latent space anonymization methods, such as…
Metric Oja Depth, New Statistical Tool for Estimating the Most Central Objects
Vida Zamanifarizhandi, Joni Virta
The Oja depth (simplicial volume depth) is one of the classical statistical techniques for measuring the central tendency of data in multivariate space. Despite the widespread emer…
A method for sparse and robust independent component analysis
Lauri Heinonen, Joni Virta
This work presents sparse invariant coordinate selection, SICS, a new method for sparse and robust independent component analysis. SICS is based on classical invariant coordinate s…
Methods for generating and evaluating synthetic longitudinal patient data: a systematic review
Katariina Perkonoja, Kari Auranen, Joni Virta
The rapid growth in data availability has facilitated research and development, yet not all industries have benefited equally due to legal and privacy constraints. The healthcare s…
On the distribution of isometric log-ratio transformations under extra-multinomial count data
Noora Kartiosuo, Joni Virta, Jaakko Nevalainen +2
Compositional data arise when count observations are normalised into proportions adding up to unity. To allow use of standard statistical methods, compositional proportions can be…