Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cluster-weighted latent class modeling   Usually in Latent Class Analysis (LCA), external predictors are taken to be
cluster conditional probability predictors (LC models with covariates), and/or
score conditional probability predictors (LC regression models). In such cases,
their distribution is not of interest. Class specific distribution is of
interest in the distal outcome model, when the distribution of the external
variable(s) is assumed to dependent on LC membership. In this paper, we
consider a more general formulation, typical in cluster-weighted models, which
embeds both the latent class regression and the distal outcome models. This
allows us to test simultaneously both whether the distribution of the
covariate(s) differs across classes, and whether there are significant direct
effects of the covariate(s) on the indicators, by including most of the
information about the covariate(s) - latent variable relationship. We show the
advantages of the proposed modeling approach through a set of population
studies and an empirical application on assets ownership of Italian households.

13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org