A grouping or blocking of observations can be achieved by using categorical or dummy variables. Examples of categorical variables include gender, country of origin, job title and experimental treatment. This should be contrasted with ordinal variables, such as age class, highest degree attained or a score on a 5-point scale with values comprised between strongly agree and strongly disagree. The...

A ubiquitous goal in plasma-enhanced chemical vapour deposition (PECVD) is to describe the correlation between film properties and categorical and quantitative input variables. The correlations within the high-dimensional parameter space are described using a multivariate model. Bayesian group analysis is employed to assess the grouping structures of the set of data vectors. This allows to iden...

If a DEA model has a mix of categorical and continuous variables a standard LP formulation can still be used by entering all combinations of categorical and continuous variables as different types of inputs and/or outputs. Most units will then not have positive levels of all variables. The implications for selection of peers are investigated. Peers can have the same or fewer types of inputs tha...

A model for analyzing multiple categorical dependent variables is presented and developed for use in organizational research. A primary example occurs in the foreign market entry literature, where choice of ownership (majority, equal, or minority) and “function” (acquisition or joint venture) are simultaneously endogenous; only separate univariate ownership-based and function-based choice model...

Statistical models usually require vector representations of categorical variables, using for instance one-hot encoding. This strategy breaks down when the number categories grows, as it creates high-dimensional feature vectors. Additionally, string entries, encoding does not capture information in their representation.Here, we seek low-dimensional high-cardinality variables. Ideally, these sho...

Materials and methods Retrospective analysis of 195 patients. The fuzzy model determined a DPN degree score (0-10) by the combination of fuzzy sets derived from clinical variables (sensorial modalities and a set of DPN-related symptoms), using if-then rules to combine the inputs with the output sets (Mamdani process), with membership functions determined by a team of 4 DPN specialists. The MCA ...

Many regression problems exhibit a natural grouping among predictor variables. Examples are groups of dummy variables representing categorical variables, or present and lagged values of time series data. Since model selection in such cases typically aims for selecting groups of variables rather than individual covariates, an extension of the popular least angle regression (LARS) procedure to gr...