Transition Models for Analyzing Longitudinal Data with Bivariate Mixed Ordinal and Nominal Responses
Authors
Abstract:
In many longitudinal studies, nominal and ordinal mixed bivariate responses are measured. In these studies, the aim is to investigate the effects of explanatory variables on these time-related responses. A regression analysis for these types of data must allow for the correlation among responses during the time. To analyze such ordinal-nominal responses, using a proposed weighting approach, an ordinal and nominal mixed transition model is proposed and then maximum likelihood method is used to find the parameter estimates. The likelihood function in this method is partitioned to make possible the use of existing software. Social-economical and political consequences arising from Iranian unemployment in the community and necessity of familiarity with the labor force characteristics particularly identification of the structure of changes in economic activity status of the Iranian population are important in order to achieve the objectives of social-economic and cultural development plans of the country. Data of Labor Force Survey in Iran are in a longitudinal form... [To continue please click here]
similar resources
Beta - Binomial and Ordinal Joint Model with Random Effects for Analyzing Mixed Longitudinal Responses
The analysis of discrete mixed responses is an important statistical issue in various sciences. Ordinal and overdispersed binomial variables are discrete. Overdispersed binomial data are a sum of correlated Bernoulli experiments with equal success probabilities. In this paper, a joint model with random effects is proposed for analyzing mixed overdispersed binomial and ordinal longitudinal respo...
full textLatent-variable models for longitudinal data with bivariate ordinal outcomes.
We use the concept of latent variables to derive the joint distribution of bivariate ordinal outcomes, and then extend the model to allow for longitudinal data. Specifically, we relate the observed ordinal outcomes using threshold values to a bivariate latent variable, which is then modelled as a linear mixed model. Random effects terms are used to tie all together repeated observations from th...
full textFlexible marginalized models for bivariate longitudinal ordinal data.
Random effects models are commonly used to analyze longitudinal categorical data. Marginalized random effects models are a class of models that permit direct estimation of marginal mean parameters and characterize serial correlation for longitudinal categorical data via random effects (Heagerty, 1999). Marginally specified logistic-normal models for longitudinal binary data. Biometrics 55, 688-...
full textBayesian Mixture Models With Focused Clustering for Mixed Ordinal and Nominal Data
In some contexts, mixture models can fit certain variables well at the expense of others in ways beyond the analyst’s control. For example, when the data include some variables with non-trivial amounts of missing values, the mixture model may fit the marginal distributions of the nearly and fully complete variables at the expense of the variables with high fractions of missing data. Motivated b...
full textGeneralized Linear Mixed Models for Nominal Data
Nominal variables include unordered polytomous variables and permutations. An unordered polytomous response is one among a set of categories whereas a permutation is an ordering of categories. The categories are nominal in the sense that they do not possess an inherent ordering shared by all units as is assumed for ordinal variables. Using decision terminology, we will refer to the categories a...
full textMultilevel Models for Ordinal and Nominal Variables
Reflecting the usefulness of multilevel analysis and the importance of categorical outcomes in many areas of research, generalization of multilevel models for categorical outcomes has been an active area of statistical research. For dichotomous response data, several approaches adopting either a logistic or probit regression model and various methods for incorporating and estimating the influen...
full textMy Resources
Journal title
volume 5 issue 1
pages 75- 94
publication date 2008-09
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023