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On the role of latent variable models in the era of big data

Author

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  • Bartolucci, Francesco
  • Bacci, Silvia
  • Mira, Antonietta

Abstract

We discuss how latent variable models are useful to deal with the complexities of big data from different perspectives: simplification of data structure; flexible representation of dependence between variables; reduction of selection bias. Problems involved in parameter estimation are also discussed.

Suggested Citation

  • Bartolucci, Francesco & Bacci, Silvia & Mira, Antonietta, 2018. "On the role of latent variable models in the era of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 165-169.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:165-169
    DOI: 10.1016/j.spl.2018.02.023
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    References listed on IDEAS

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    1. Francesco Bartolucci & Monia Lupparelli, 2016. "Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 216-228, March.
    2. Turner, Rolf, 2008. "Direct maximization of the likelihood of a hidden Markov model," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4147-4160, May.
    3. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
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    Cited by:

    1. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.

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