Micro–macro multilevel latent class models with multiple discrete individual-level variables
نویسندگان
چکیده
منابع مشابه
Multilevel Latent Class Models
The latent class (LC) models that have been developed so far assume that observations are independent. Parametric and nonparametric random-coefficient LC models are proposed here, which will make it possible to modify this assumption. For example, the models can be used for the analysis of data collected with complex sampling designs, data with a multilevel structure, and multiple-group data fo...
متن کاملStepwise Latent Class Models for Explaining Group-Level Outcomes Using Discrete Individual-Level Predictors.
Explaining group-level outcomes from individual-level predictors requires aggregating the individual-level scores to the group level and correcting the group-level estimates for measurement errors in the aggregated scores. However, for discrete variables it is not clear how to perform the aggregation and correction. It is shown how stepwise latent class analysis can be used to do this. First, a...
متن کاملMultilevel latent class models with dirichlet mixing distribution.
Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social science and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this article, we consider multilevel lat...
متن کاملLearning Deep Generative Models with Discrete Latent Variables
There have been numerous recent advancements on learning deep generative models with latent variables thanks to the reparameterization trick that allows to train deep directed models effectively. However, since reparameterization trick only works on continuous variables, deep generative models with discrete latent variables still remain hard to train and perform considerably worse than their co...
متن کاملLearning Deep Generative Models With Discrete Latent Variables
There have been numerous recent advancements on learning deep generative models with latent variables thanks to the reparameterization trick that allows to train deep directed models effectively. However, since reparameterization trick only works on continuous variables, deep generative models with discrete latent variables still remain hard to train and perform considerably worse than their co...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Data Analysis and Classification
سال: 2016
ISSN: 1862-5347,1862-5355
DOI: 10.1007/s11634-016-0234-1