Identifiability in robust estimation of tree structured models-Reference-Cited by-同舟云学术

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Identifiability in robust estimation of tree structured models

Published:2024-02-01 Issue:1 Volume:30 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Casanellas Marta¹,Garrote-López Marina²,Zwiernik Piotr³

Affiliation:

1. Institut de Matemàtiques de la UPC-BarcelonaTech (IMTech), Universitat Politècnica de Catalunya, Barcelona, Spain

2. Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany

3. Department of Statistical Sciences, University of Toronto, Toronto, Canada

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference25 articles.

1. Anandkumar, A., Chaudhuri, K., Hsu, D.J., Kakade, S.M., Song, L. and Zhang, T. (2011). Spectral methods for learning multivariate latent tree structure. In Advances in Neural Information Processing Systems 2025–2033.

2. Badrinarayanan, V., Budvytis, I. and Cipolla, R. (2013). Semi-supervised video segmentation using tree structured graphical models. IEEE Trans. Pattern Anal. Mach. Intell. 35 2751–2764. 10.1109/TPAMI.2013.54

3. Bresler, G. and Karzand, M. (2020). Learning a tree-structured Ising model in order to make predictions. Ann. Statist. 48 713–737. 10.1214/19-AOS1808

4. Buneman, P. (1971). The recovery of trees from measures of dissimilarity. In Mathematics in the Archaeological and Historical Sciences 387–395. Edinburgh University Press.

5. Chang, J.T. (1996). Full reconstruction of Markov models on evolutionary trees: Identifiability and consistency. Math. Biosci. 137 51–73. 10.1016/S0025-5564(96)00075-2

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