References

  • Burdick, R. K., Borror, C. M., and Montgomery, D. C. (2005), Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models, Philadelphia, PA and Alexandria, VA: SIAM and ASA.

  • Davidian, M. and Giltinan, D. M. (1995), Nonlinear Models for Repeated Measurement Data, New York: Chapman & Hall.

  • Demidenko, E. (2004), Mixed Models: Theory and Applications, New York: John Wiley & Sons.

  • Diggle, P. J., Liang, K.-Y., and Zeger, S. L. (1994), Analysis of Longitudinal Data, Oxford: Clarendon Press.

  • Laird, N. M. and Ware, J. H. (1982), “Random-Effects Models for Longitudinal Data,” Biometrics, 38, 963–974.

  • Liang, K.-Y. and Zeger, S. L. (1986), “Longitudinal Data Analysis Using Generalized Linear Models,” Biometrika, 73, 13–22.

  • Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. (2006), SAS for Mixed Models, Second Edition, Cary, NC: SAS Press.

  • Milliken, G. A. and Johnson, D. E. (1992), Analysis of Messy Data, Volume 1: Designed Experiments, New York: Chapman & Hall.

  • Molenberghs, G. and Verbeke, G. (2005), Models for Discrete Longitudinal Data, New York: Springer.

  • Verbeke, G. and Molenberghs, G., eds. (1997), Linear Mixed Models in Practice: A SAS-Oriented Approach, New York: Springer.

  • Verbeke, G. and Molenberghs, G. (2000), Linear Mixed Models for Longitudinal Data, New York: Springer.

  • Vonesh, E. F. and Chinchilli, V. M. (1997), Linear and Nonlinear Models for the Analysis of Repeated Measurements, New York: Marcel Dekker.

  • Zeger, S. L. and Liang, K.-Y. (1986), “Longitudinal Data Analysis for Discrete and Continuous Outcomes,” Biometrics, 42, 121–130.