Zobrazeno 1 - 10
of 174
pro vyhledávání: '"Tsagris, Michail"'
In 2020, two novel distributions for the analysis of directional data were introduced: the spherical Cauchy distribution and the Poisson kernel-based distribution. This paper provides a detailed exploration of both distributions within various analyt
Externí odkaz:
http://arxiv.org/abs/2409.03292
Autor:
Tsagris, Michail
Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficien
Externí odkaz:
http://arxiv.org/abs/2403.19835
Autor:
Tsagris, Michail, Alzeley, Omar
We introduce a novel family of projected distributions on the circle and the sphere, namely the circular and spherical projected Cauchy distributions, as promising alternatives for modelling circular and spherical data. The circular distribution enco
Externí odkaz:
http://arxiv.org/abs/2302.02468
Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic point of view, classical PCA can be formulated in terms of operations on a multivariate Gaussian like
Externí odkaz:
http://arxiv.org/abs/2211.03181
We introduce a new R package useful for inference about network count time series. Such data are frequently encountered in statistics and they are usually treated as multivariate time series. Their statistical analysis is based on linear or log linea
Externí odkaz:
http://arxiv.org/abs/2211.02582
Autor:
Tsagris, Michail
We present a new model for analyzing compositional data with structural zeros. Inspired by \cite{butler2008} who suggested a model in the presence of zero values in the data we propose a model that treats the zero values in a different manner. Instea
Externí odkaz:
http://arxiv.org/abs/2208.13073
Autor:
Tsagris, Michail
Publikováno v:
Mathematics 2022, 10(15), 2604
The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MM
Externí odkaz:
http://arxiv.org/abs/2012.00113
Autor:
Papadaki, Ioanna, Tsagris, Michail
It is customary for researchers and practitioners to fit linear models in order to predict NBA player's salary based on the players' performance on court. On the contrary, we focus on the players salary share (with regards to the team payroll) by fir
Externí odkaz:
http://arxiv.org/abs/2007.14694
Feature selection for predictive analytics is the problem of identifying a minimal-size subset of features that is maximally predictive of an outcome of interest. To apply to molecular data, feature selection algorithms need to be scalable to tens of
Externí odkaz:
http://arxiv.org/abs/2004.00281
Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression
Externí odkaz:
http://arxiv.org/abs/2002.05137