Obtaining marginal estimates from conditional categorical repeated measurements models with missing data
✍ Scribed by J. K. Lindsey
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 101 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
✦ Synopsis
The most commonly used models for categorical repeated measurement data are log-linear models. Not only are they easy to "t with standard software but they include such useful models as Markov chains and graphical models. However, these are conditional models and one often also requires the marginal probabilities of responses, for example, at each time point in a longitudinal study. Here a simple method of matrix manipulation is used to derive the maximum likelihood estimates of the marginal probabilities from any such conditional categorical repeated measures model. The technique is applied to the classical Muscatine data set, taking into account the dependence of missingness on previous observed values, as well as serial dependence and a random e!ect.