10 papers
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…
Evaluation metrics for temporal preservation in synthetic longitudinal patient data
Katariina Perkonoja, Parisa Movahedi, Antti Airola +2
This study introduces a set of metrics for evaluating temporal preservation in synthetic longitudinal patient data, defined as artificially generated data that mimic real patients'…
Asymptotic testing of covariance separability for matrix elliptical data
Joni Virta, Takeru Matsuda
We propose a new asymptotic test for the separability of a covariance matrix. The null distribution is valid in wide matrix elliptical model that includes, in particular, both matr…
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…