Karel Hron

CONTACT

hronk@seznam.cz

ORDCID number: 0000-0002-1847-6598

BIO

I'm an Associate Professor of Applied Mathematics at Palacký University, Czech Republic, and I would define myself as applied statistician working in the field of compositional data analysis. Compositional data are multivariate observations that carry relative information. They are measured in units like proportions, percentages, mg/l, mg/kg, ppm, cps and so on. Due to their specific features, particularly scale invariance and non-negativity, the statistical analysis of compositional data must obey the geometry of a simplex space, the so called Aitchison geometry. In order to enable compositional data processing using standard statistical methods, compositions consisting of D components can be conveniently expressed by means of real vectors of D-1 log-ratio coordinates. Meaningful interpretability of such coordinates is of primary importance in practical applications. I have worked in the last 14 years intensively in the field, in developing the methodology itself as well in promoting compositional data analysis in a wide range of applications including geochemistry, chemometrics, omics sciences and time use epidemiology. Geochemical data, which are the most relevant for the aim of the project, are of my primary interest as also the concept of the logratio methodology was motivated by problems from geochemistry and soil science in general. Together with my colleagues, particularly from my home institution, Palacký University, and Vienna University of Technology, we have published cca. 90 papers and book chapters about the topic, but the main fruitage of our efforts is definitely the book Filzmoser et al. (2018) where main results of our scientific efforts are summarized.

SELECTED PUBLICATIONS

I'm an Associate Professor of Applied Mathematics at Palacký University, Czech Republic, and I would define myself as applied statistician working in the field of compositional data analysis. Compositional data are multivariate observations that carry relative information. They are measured in units like proportions, percentages, mg/l, mg/kg, ppm, cps and so on. Due to their specific features, particularly scale invariance and non-negativity, the statistical analysis of compositional data must obey the geometry of a simplex space, the so called Aitchison geometry. In order to enable compositional data processing using standard statistical methods, compositions consisting of D components can be conveniently expressed by means of real vectors of D-1 log-ratio coordinates. Meaningful interpretability of such coordinates is of primary importance in practical applications. I have worked in the last 14 years intensively in the field, in developing the methodology itself as well in promoting compositional data analysis in a wide range of applications including geochemistry, chemometrics, omics sciences and time use epidemiology. Geochemical data, which are the most relevant for the aim of the project, are of my primary interest as also the concept of the logratio methodology was motivated by problems from geochemistry and soil science in general. Together with my colleagues, particularly from my home institution, Palacký University, and Vienna University of Technology, we have published cca. 90 papers and book chapters about the topic, but the main fruitage of our efforts is definitely the book Filzmoser et al. (2018) where main results of our scientific efforts are summarized.

SELECTED PUBLICATIONS

-Filzmoser, P., Hron, K., Templ, M. (2018) Applied compositional data analysis, Springer Series in Statistics. Springer, Cham.

-Filzmoser, P., Hron, K., Reimann, C. (2009) Univariate statistical analysis of environmental (compositional) data: Problems and possibilities. Science of the Total Environment, 407, No. 23, 6100-6108.

-Hron, K., Filzmoser, P., Thompson, K. (2012) Linear regression with compositional explanatory variables. Journal of Applied Statistics, 39, No. 5, 1115-1128.

-Bábek, O., Grygar, T.M., Faměra, M., Hron, K., Nováková, T., Sedláček, J. (2015) Geochemical background in polluted river sediments: how to separate the effects of sediment provenance and grain size with statistical rigour? Catena, 135, 240-253.

-Reimann, C., Filzmoser, P., Hron, K., Kynčlová, P., Garrett, R (2017) A new method for correlation analysis of compositional (environmental) data – a worked example. Science of the Total Environment, 607-608, 965-971.

-Hron, K., Engle, M., Filzmoser, P., Fišerová, E. (2020) Weighted symmetric pivot coordinates for compositional data with geochemical applications. Mathematical Geosciences, DOI: 10.1007/s11004-020-09862-5