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Discretized and Aggregated: Modeling Dive Depth of Harbor Seals from Ordered Categorical Data with Temporal Autocorrelation

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  • Megan D. Higgs
  • Jay M. Ver Hoef

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  • Megan D. Higgs & Jay M. Ver Hoef, 2012. "Discretized and Aggregated: Modeling Dive Depth of Harbor Seals from Ordered Categorical Data with Temporal Autocorrelation," Biometrics, The International Biometric Society, vol. 68(3), pages 965-974, September.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:3:p:965-974
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01710.x
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    References listed on IDEAS

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    1. Jay Ver Hoef & Josh London & Peter Boveng, 2010. "Fast computing of some generalized linear mixed pseudo-models with temporal autocorrelation," Computational Statistics, Springer, vol. 25(1), pages 39-55, March.
    2. Higgs, Megan Dailey & Hoeting, Jennifer A., 2010. "A clipped latent variable model for spatially correlated ordered categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1999-2011, August.
    3. Li, Yonghai & Schafer, Daniel W., 2008. "Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3474-3492, March.
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    Cited by:

    1. Kathryn M. Irvine & T. J. Rodhouse & Ilai N. Keren, 2016. "Extending Ordinal Regression with a Latent Zero-Augmented Beta Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 619-640, December.

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