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Umeå universitet
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Huvudmenyn dold.
Personalbild Tetiana Gorbach

Tetiana Gorbach

Kontakt

E-post
tetiana.gorbach@umu.se
Plats
Samhällsvetarhuset, Biblioteksgränd 6 Umeå universitet, 901 87 Umeå
Telefon
090-786 72 01

Verksam vid

Anknytning
Universitetslektor (tjänstledig) vid Handelshögskolan vid Umeå universitet Enhet: Statistik
Anknytning
Universitetslektor vid Handelshögskolan vid Umeå universitet Enhet: Statistik

Projekt: "Selection bias in ageing studies: novel statistical methods for analyses of incomplete datasets" (Fortes internationell postdok). 2 300 000 SEK. Huvudsökande. 2022-2023.

Projekt: "Machine learning to study causality with big datasets: towards methods yielding valid statistical conclusions". Marianne och Marcus Wallenbergs Stilftelse. 6 000 000 SEK. Medsökande. Projektledare: Xavier de Luna. 2022-2027.

DyNAMiC: A prospective longitudinal study of dopamine and brain connectomes: A new window into cognitive aging
Journal of Neuroscience Research, John Wiley & Sons 2022, Vol. 100, (6) : 1296-1320
Nordin, Kristin; Gorbach, Tetiana; Pedersen, Robin; et al.
When functional blurring becomes deleterious: Reduced system segregation is associated with less white matter integrity and cognitive decline in aging
NeuroImage, Academic Press 2021, Vol. 242
Pedersen, Robin; Geerligs, Linda; Andersson, Micael; et al.
Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents' brains
Brain and Behavior, John Wiley & Sons 2021, Vol. 11, (2)
Ruotsalainen, Ilona; Glerean, Enrico; Karvanen, Juha; et al.
A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding
Brain Connectivity, Mary Ann Liebert 2020, Vol. 10, (5) : 202-211
Gorbach, Tetiana; Lundquist, Anders; de Luna, Xavier; et al.
Longitudinal association between hippocampus atrophy and episodic-memory decline in non-demented APOE ε4 carriers
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, John Wiley & Sons 2020, Vol. 12, (1)
Gorbach, Tetiana; Pudas, Sara; Bartrés-Faz, David; et al.
Physical activity, aerobic fitness, and brain white matter: Their role for executive functions in adolescence
Developmental Cognitive Neuroscience, Elsevier 2020, Vol. 42
Ruotsalainen, Ilona; Gorbach, Tetiana; Perkola, Jaana; et al.
Methods for longitudinal brain imaging studies with dropout
Statistical studies, 54
Gorbach, Tetiana
Aerobic fitness, but not physical activity, is associated with grey matter volume in adolescents
Behavioural Brain Research, Elsevier 2019, Vol. 362 : 122-130
Ruotsalainen, Ilona; Renvall, Ville; Gorbach, Tetiana; et al.
Inference for partial correlation when data are missing not at random
Statistics and Probability Letters, Elsevier 2018, Vol. 141 : 82-89
Gorbach, Tetiana; de Luna, Xavier
Longitudinal association between hippocampus atrophy and episodic-memory decline
Neurobiology of Aging, Vol. 51 : 167-176
Gorbach, Tetiana; Pudas, Sara; Lundquist, Anders; et al.
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Gorbach, Tetiana; de Luna, Xavier; Waernbaum, Ingeborg; et al.
A Hierarchical Bayesian Mixture Modeling Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding
Gorbach, Tetiana; Lundquist, Anders; de Luna, Xavier; et al.
Bayesian mixture modeling for longitudinal fMRI connectivity studies with dropout
Gorbach, Tetiana; Lundquist, Anders; de Luna, Xavier; et al.
Valid causal inference: model selection and sensitivity to unobserved confounding in high-dimensional settings
Moosavi, Niloofar; Gorbach, Tetiana; de Luna, Xavier
A note on sensitivity analysis for post-machine learning causal inference
Moosavi, Niloofar; Gorbach, Tetiana; de Luna, Xavier
Physical activity is positively related tolocal functional connectivity in adolescents’ brains
Ruotsalainen, Ilona; Glerean, Enrico; Karvanen, Juha; et al.
Visa publikationer i DiVA

Forskargrupper

Gruppmedlem
Stat4Reg

Forskningsprojekt

1 juni 2022 till 31 december 2026
Maskininlärning för studier av orsakssamband med hjälp av stora databaser