In this paper, a data mining approach is proposed for duration prediction of the town trips (travel time) in New York City. In this regard, at first, two novel approaches, including a mathematical and a statistical approach, are proposed for grouping categorical variables with a huge number of levels. The proposed approaches work based on the cost matrix generated by repetitive post-hoc tests f...

Evidence obtained using the dynamic grouping method has shown that the grouping of an object's connected surfaces has properties characteristic of a nonlinear dynamical system. When a surface's luminance changes, one of its boundaries is perceived moving across the surface. The direction of this dynamic grouping (DG) motion indicates which of two flanking surfaces has been grouped with the chan...

This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeli...

Many real-life applications consider nominal categorical predictor variables that have a hierarchical structure, e.g. economic activity data in Official Statistics. In this paper, we focus on linear regression models built the presence of type variables, and study consolidation their categories to better tradeoff between interpretability fit model data. We propose so-called Tree based Linear Re...

Definitions and Distinctions First, it is important to distinguish between categorical variables and continuous variables. Categorical variables are those with two values (i.e., binary, dichotomous) or those with a few ordered categories. Examples might include gender, dead vs. alive, audited vs. not audited, or variables with few response options like “never,” “sometimes,” or “always.” Continu...

Definitions and Distinctions First, it is important to distinguish between categorical variables and continuous variables. Categorical variables are those with two values (i.e., binary, dichotomous) or those with a few ordered categories. Examples might include gender, dead vs. alive, audited vs. not audited, or variables with few response options like “never,” “sometimes,” or “always.” Continu...