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Nonlinear latent variable models are specified that include quadratic forms and interactions of latent regressor variables as special cases. To estimate the parameters, the models are put in a… (More)

Casual Inference in the Social and Behavioral Sciences. Missing Data. Specification and Estimation of Mean Structures. The Analysis of Contingency Tables. Latent Class Models. Panel Analysis for… (More)

Models and parameters of finite mixtures of multivariate normal densities conditional on regressor variables are specified and estimated. We consider mixtures of multivariate normals where the… (More)

Models of finite mixtures of normal densities conditional on regressor variables are specified and estimated. The authors consider mixtures of multivariate normals where the expected value for each… (More)

Three different discriminant techniques are applied and compared to analyze a complex data set of credit risks. A large sample is split into a training, a validation, and a test sample. The dependent… (More)

Abstract A nonlinear mean- and covariance-structure model for one or more groups is constructed. The model subsumes the usual linear model considered in the literature. It is then shown how to… (More)

The analysis of moment structures originated with the factor analysis model and with some simple pattern hypotheses concerning equality of elements of mean vectors and covariance matrices. They have… (More)

- Jochen Kruppa, Alexandra Schwarz, Gerhard Arminger, Andreas Ziegler
- Expert Syst. Appl.
- 2013

Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the… (More)

Using the theory of pseudo maximum likelihood estimation the asymptotic covariance matrix of maximum likelihood estimates for mean and covariance structure models is given for the case where the… (More)

- Gerhard Arminger
- 1986

Since Coleman (1968), sociologists have increasingly used differential equation models to analyze continuous dependent variables over time. Current applications of these models include, for example,… (More)