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Comments on: Data integration via analysis of subspaces (DIVAS)

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An Invited Paper to this article was published on 14 March 2024

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References

  • Cai TT, Zhang A (2018) Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics. Ann Stat 46(1):60–89

    Article  MathSciNet  Google Scholar 

  • Feng Q, Jiang M, Hannig J, Marron J (2018) Angle-based joint and individual variation explained. J Multivar Anal 166:241–265

    Article  MathSciNet  Google Scholar 

  • Grant M, Boyd S (2008) Graph implementations for nonsmooth convex programs. In: Blondel V, Boyd S, Kimura H (eds) Recent advances in learning and control. Lecture notes in control and information sciences. Springer, Berlin, pp 95–110

    Chapter  Google Scholar 

  • Grant M, Boyd S (2014) CVX: Matlab software for disciplined convex programming, version 2.1. https://cvxr.com/cvx

  • Gavish M, Donoho DL (2017) Optimal shrinkage of singular values. IEEE Trans Inf Theory 63(4):2137–2152

    Article  MathSciNet  Google Scholar 

  • Gaynanova I, Li G (2019) Structural learning and integrative decomposition of multi-view data. Biometrics 75(4):1121–1132

    Article  MathSciNet  Google Scholar 

  • Lock EF, Park JY, Hoadley KA (2022) Bidimensional linked matrix factorization for pan-omics pan-cancer analysis. Ann Appl Stat 16(1):193

    Article  MathSciNet  Google Scholar 

  • Owen AB, Perry PO (2009) Bi-cross-validation of the SVD and the nonnegative matrix factorization. Ann Appl Stat 3(2):564–594

    Article  MathSciNet  Google Scholar 

  • Wedin P-Å (1972) Perturbation bounds in connection with singular value decomposition. BIT Numer Math 12:99–111

    Article  MathSciNet  Google Scholar 

  • Yi S, Wong RKW, Gaynanova I (2023) Hierarchical nuclear norm penalization for multi-view data integration. Biometrics 79(4):2933–2946

    Article  MathSciNet  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA

    Irina Gaynanova

  2. Department of Statistics, Texas A &M University, 155 Ireland St, College Station, TX, 77843, USA

    Renat Sergazinov

Authors
  1. Irina Gaynanova
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  2. Renat Sergazinov
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Correspondence to Irina Gaynanova.

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Gaynanova, I., Sergazinov, R. Comments on: Data integration via analysis of subspaces (DIVAS). TEST (2024). https://doi.org/10.1007/s11749-024-00936-8

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