Introduction to Mixed Modeling Procedures


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. 2nd ed. Cary, NC: SAS Institute Inc.

  • Milliken, G. A., and Johnson, D. E. (1992). Designed Experiments. Vol. 1 of Analysis of Messy Data. Reprint edition. 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.