Graphical Models for Clustered Binary and Continuous Responses
Graphical models for clustered data mixed with discrete and continuous responses are developed. Discrete responses are assumed to be regulated by some latent continuous variables and particular link functions are used to describe the regulatory mechanisms. Inferential procedures are constructed using the full-information maximum likelihood estimation and observed/empirical Fisher information matrices. Implementation is carried out by stochastic versions of the generalized EM algorithm. As an illustrative application, clustered data from a developmental toxicity study is re-investigated using the directed graphical model and the proposed algorithms. A new interesting directed association between two mixed outcomes reveals. The proposed methods also apply to cross-sectional data with discrete and continuous responses.
KeywordsConditional Expectation Stochastic Version Latent Continuous Variable SAEM Algorithm Directed Graphical Model
Unable to display preview. Download preview PDF.
- 1.Amemiya T (1985) Advanced econometrics. Harvard University Press, CambridgeGoogle Scholar
- 4.Broniatowski M, Celeux G, Diebolt J (1983) Reconnaissance de mélanges de densités par un algorithme d’apprentissage probabiliste. In: Diday E (ed) Data analysis and informatics, vol 3. Amsterdam, North Holland, pp 359–374Google Scholar
- 7.Celeux G, Diebolt J (1983) A probabilistic teacher algorithm for iterative maximum likelihood estimation. In: Bock H (ed) Classification and related methods of data analysis. Amsterdam, North Holland, pp 617–623Google Scholar
- 8.Celeux G, Diebolt J (1985) The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Comput Stat Q 2:73–82Google Scholar