We review the theory of the latent Markov model for univariate and multivariate ordinal responses. They frequently occur when the data are related to social and medical surveys, and also in other fields of application where the order among the response categories can be made according to subject matter knowledge. We review how to assess the influence of covariates according to a chosen link function based on local, global, or continuation logits, or by an ordered probit link function. In this way, the model parameters can retain the ordinal structure imposed by the data. We show the statistical algorithms required for the parameters estimation and through examples based on real data we show how predictions can be achieved.
Pennoni, F. (2017). A review of panel data models with a Markov dependent structure for univariate and multivariate ordinal responses. In Book of Abstracts Cfe-Cmstatistics 2017 (pp.61-61).
A review of panel data models with a Markov dependent structure for univariate and multivariate ordinal responses
Pennoni, F.
Primo
2017
Abstract
We review the theory of the latent Markov model for univariate and multivariate ordinal responses. They frequently occur when the data are related to social and medical surveys, and also in other fields of application where the order among the response categories can be made according to subject matter knowledge. We review how to assess the influence of covariates according to a chosen link function based on local, global, or continuation logits, or by an ordered probit link function. In this way, the model parameters can retain the ordinal structure imposed by the data. We show the statistical algorithms required for the parameters estimation and through examples based on real data we show how predictions can be achieved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.