Multiple Factorial Analysis of Symbolic Data

Symbolic Analysis of Financial Data

We have as input two portfolio of stocks managed by ING bank (North Holland). Each portfolio and each stock is described by several variables every open day of a week and during eight weeks. A portfolio is characterised by variables as its value, its performance, a stock is characterised by variable as its value, its number in the portfolio and its performance. In this paper, we study a symboli...

Greatest Factorial Factorization and Symbolic Summation

At present the most general algebraic and algorithmic frame for discussing the problem of indefinite summation is provided by the work of Karr (1981, 1985). His method, working over ΠΣ-fields which are certain difference field extensions of a constant field K, can be viewed as a summation analogue to Risch’s integration method. A difference field simply is a field F together with an automorphis...

Multidimensional Interval-Data: Metrics and Factorial Analysis

Statistical units described by interval-valued variables represent a special case of Symbolic Objects, where all descriptors are quantitative variables. In this context, the paper presents two different metrics in R for interval-valued data that are based on the definition of the Hausdorff distance in R. Hausdorff distance in R (for any p ≥ 1) is a L∞ norm between pairs of closed sets. However,...

Model combining in factorial data analysis

ANOVA is a statistical tool commonly used to study factor effects. Usually a model is selected through hypothesis testing or according to some model selection criteria. Both approaches have difficulties in finding the best model. When the goal is to estimate the cell means rather than to select a model, we proposed an algorithm, ARM, to convexly combine the candidate models. Simulation and data...

Ordinal and symbolic data analysis