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ORCIDMichail Tsagris
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2020 – today
- 2024
[j14]Michail Tsagris
, Abdulaziz Alenazi:
An investigation of hypothesis testing procedures for circular and spherical mean vectors. Commun. Stat. Simul. Comput. 53(3): 1387-1408 (2024)- [j13]Mirko Armillotta, Michail Tsagris, Konstantinos Fokianos:
Inference for Network Count Time Series with the R Package PNAR. R J. 15(4): 255-269 (2024) - [j12]Aisha Fayomi, Yannis Pantazis, Michail Tsagris, Andrew T. A. Wood:
Cauchy robust principal component analysis with applications to high-dimensional data sets. Stat. Comput. 34(1): 26 (2024) - 2023
- [j11]Kleanthi Lakiotaki, Zacharias Papadovasilakis, Vincenzo Lagani, Stefanos Fafalios, Paulos Charonyktakis, Michail Tsagris, Ioannis Tsamardinos:
Automated machine learning for genome wide association studies. Bioinform. 39(9) (2023) - [j10]Michail Tsagris, Abdulaziz Alenazi, Connie Stewart:
Flexible non-parametric regression models for compositional response data with zeros. Stat. Comput. 33(5): 106 (2023) - 2022
- [j9]Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki, Ioannis Tsamardinos:
The $\gamma$γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data. IEEE ACM Trans. Comput. Biol. Bioinform. 19(2): 1214-1224 (2022) - 2020
- [j8]P. J. Paine, S. P. Preston, Michail Tsagris, Andrew T. A. Wood:
Spherical regression models with general covariates and anisotropic errors. Stat. Comput. 30(1): 153-165 (2020) - [i4]Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki, Ioannis Tsamardinos:
A generalised OMP algorithm for feature selection with application to gene expression data. CoRR abs/2004.00281 (2020) - [i3]Michail Tsagris:
The FEDHC Bayesian network learning algorithm. CoRR abs/2012.00113 (2020)
2010 – 2019
- 2019
- [j7]Michail Tsagris:
Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Appl. Artif. Intell. 33(2): 101-123 (2019) - 2018
- [j6]Kleanthi Lakiotaki, Nikolaos Vorniotakis, Michail Tsagris, Georgios Georgakopoulos, Ioannis Tsamardinos:
BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. Database J. Biol. Databases Curation 2018: bay011 (2018) - [j5]Michail Tsagris, Vincenzo Lagani, Ioannis Tsamardinos:
Feature selection for high-dimensional temporal data. BMC Bioinform. 19(1): 17:1-17:14 (2018) - [j4]Michail Tsagris, Giorgos Borboudakis, Vincenzo Lagani, Ioannis Tsamardinos:
Constraint-based causal discovery with mixed data. Int. J. Data Sci. Anal. 6(1): 19-30 (2018) - [j3]Michail Tsagris, Giorgos Borboudakis, Vincenzo Lagani, Ioannis Tsamardinos:
Correction to: Constraint-based causal discovery with mixed data. Int. J. Data Sci. Anal. 6(1): 31 (2018) - [j2]P. J. Paine, S. P. Preston, Michail Tsagris, Andrew T. A. Wood:
An elliptically symmetric angular Gaussian distribution. Stat. Comput. 28(3): 689-697 (2018) - [i2]Michail Tsagris, Ioannis Tsamardinos:
Feature selection with the R package MXM. F1000Research 7: 1505 (2018) - [i1]Michail Tsagris, Manos Papadakis:
Taking R to its limits: 70+ tips. PeerJ Prepr. 6: e26605 (2018) - 2016
- [j1]Michail Tsagris, Simon Preston, Andrew T. A. Wood:
Improved Classification for Compositional Data Using the α-transformation. J. Classif. 33(2): 243-261 (2016) - 2013
- [c1]Michail Tsagris, Jonathan M. Garibaldi:
Modelling distributions of the temporal membership grades for non-stationary fuzzy sets. FUZZ-IEEE 2013: 1-8
Coauthor Index
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