Projects per year
Personal profile
Research Interests
My work has been focused on extending modern machine learning techniques to classical statistical problems, in particular to Survival Analysis. To do this, I have worked with kernel methods together with the theory of counting processes to design more robust non-parametric tests that can be applied to the right-censored data framework. As a recent interest I am focused on studying how the techniques I have developed extend to other important statistical problems.
Key Words
Kernel Methods; Non-parametric Statistics; Asymptotic Statistics; Survival Analysis.
Profession
No Information
Education/Academic qualification
PhD, University of Oxford
Award Date: 29 Aug 2018
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Projects
- 2 Active
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IT24I0144: Intrusion.aware: Plataforma integral para detectar y responder ciberataques usando inteligencia artificial responsable
Torres, R. (Director), Fernandez, T. (Alternate Director), Bórquez, D. (Researcher) & Moreno, S. (Researcher)
Agencia Nacional de Investigación y Desarrollo
16/08/24 → 16/08/26
Project: Research
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Kernelised linear tests: combining Statistics and Machine learning to boost test power.
Fernandez, T. (Principal Investigator)
Agencia Nacional de Investigación y Desarrollo
15/03/22 → 14/03/25
Project: Research
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A Kernel Log-Rank Test of Independence for Right-Censored Data
Fernández, T., Gretton, A., Rindt, D. & Sejdinovic, D., 2023, In: Journal of the American Statistical Association. 118, 542, p. 925-936 12 p.Research output: Contribution to journal › Article › peer-review
Open Access2 Scopus citations -
A reproducing kernel Hilbert space log-rank test for the two-sample problem
Fernández, T. & Rivera, N., Dec 2021, In: Scandinavian Journal of Statistics. 48, 4, p. 1384-1432 49 p.Research output: Contribution to journal › Article › peer-review
Open Access2 Scopus citations -
A kernel test for quasi-independence
Fernández, T., Xu, W., Ditzhaus, M. & Gretton, A., 2020, In: Advances in Neural Information Processing Systems. 2020-DecemberResearch output: Contribution to journal › Conference article › peer-review
2 Scopus citations -
A maximum-mean-discrepancy goodness-of-fit test for censored data
Fernández, T. & Gretton, A., 2020.Research output: Contribution to conference › Paper › peer-review
5 Scopus citations -
Kaplan-meier v-and u-statistics
Fernández, T. & Rivera, N., 2020, In: Electronic Journal of Statistics. 14, 1, p. 1872-1916 45 p.Research output: Contribution to journal › Article › peer-review
Open Access6 Scopus citations