Abstract. Logic-based methods provide a strategy for applying convex nonlinear programming to nonconvex global optimization. Such methods assume that the problem becomes convex when selected variables are fixed. The selected variables must be discrete, or else discretized if they are continuous. We provide a tutorial survey of disjunctive programming with convex relaxations, logic-based outer a...

classification trees (j48) were induced to predict the habitat requirements of tench (tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in flanders (belgium). the input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (ph, dissolved oxygen, water temperature and electric conduct...

Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of ‘traditional’ latent variable models include latent class models, item–response models, common factor models, structural equation models, mixed or random effects models and covariate measurement error models. Altho...

This paper is designed to give a complete overview of the literature that is available, as it relates to application of the Bayesian analysis model to investigate multiple group nonlinear structural equation models, also known as SEMs, including those having ordered categorical, dichotomous and categorical-dichotomous mixed variables. It will also work to summarize Bayesian multiple group nonli...

A general nonlinear structural equation model with mixed continuous and polytomous variables is analysed. A Bayesian approach is proposed to estimate simultaneously the thresholds, the structural parameters and the latent variables. To solve the computational difficulties involved in the posterior analysis, a hybrid Markov chain Monte Carlo method that combines the Gibbs sampler and the Metropo...

Several techniques exist nowadays for continuous (i.e. numerical) data analysis and modeling. However, although part of the information gathered by companies, statistical offices and other institutions is numerical, a large part of it is represented using categorical variables in ordinal or nominal scales. Techniques for model building on categorical data are required to take advantage of such ...

Ala~traet--Errors-in-variables problems are considered for the case of three variables, where the underlying relations among the noise-free variables are linear, and covariance information for the noisy variables is available. The static problem where the variables are complex (which can arise when narrowband filtering is used) is analysed in detail. Some results are also presented for the case...