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A Bayesian latent variable approach to functional principal components analysis with binary and count data

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  • Angelika Linde

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  • Angelika Linde, 2009. "A Bayesian latent variable approach to functional principal components analysis with binary and count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(3), pages 307-333, September.
    • Handle: RePEc:spr:alstar:v:93:y:2009:i:3:p:307-333
    DOI: 10.1007/s10182-009-0113-6
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    References listed on IDEAS

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    1. Sam Behseta & Robert E. Kass & Garrick L. Wallstrom, 2005. "Hierarchical models for assessing variability among functions," Biometrika, Biometrika Trust, vol. 92(2), pages 419-434, June.
    2. Michel Wedel & Wagner Kamakura, 2001. "Factor analysis with (mixed) observed and latent variables in the exponential family," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 515-530, December.
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    Cited by:

    1. Daniel Backenroth & Russell T. Shinohara & Jennifer A. Schrack & Jeff Goldsmith, 2020. "Nonnegative decomposition of functional count data," Biometrics, The International Biometric Society, vol. 76(4), pages 1273-1284, December.
    2. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
    3. Zhang, Ruizhi & Wang, Jian & Mei, Yajun, 2017. "Search for evergreens in science: A functional data analysis," Journal of Informetrics, Elsevier, vol. 11(3), pages 629-644.
    4. Bharath, Karthik, 2013. "A note on density estimation for binary sequences," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2735-2742.

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