Displaying similar documents to “Multidimensional mapping of preference data”

Statistical and decision theoretic aspects of examination assessment.

Simon French (1989)

Trabajos de Estadística

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At a recent conference in Innsbruck, held in memory of Bruno De Finetti, attention focused on one of his aphorisms: analysts should think about things. This paper seeks to do precisely that in the context of public examinations in England and Wales. It attempts to think about those quantitative things that are done to marks in the process of assessing candidate scripts. Public examinations are central to our education system: hundreds of thousands of candidates enter them every year....

Longitudinal K-sets analysis using lagged variables.

Catrien C. J. H. Bijleveld, Eeke Van der Burg (1993)

Qüestiió

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We present an application of nonlinear Generalised Canonical Analysis (GCA) for analysing longitudinal data. The application uses lagged versions of variables to accomodate the time-dependence in the measurements. The usefulness of the proposed method is illustrated in an example from developmental psychology, in which we explore the relationship between mother and child dyadic interaction during the first six months after birth, demonstrating how child behaviour can elicit mother behaviour....

A model for credit scoring: an application of discriminant analysis.

Manuel Artís, Montserrat Guillén, José M.ª Martínez (1994)

Qüestiió

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The application of statistical techniques in decision making, and more specifically for classification requirements, has proved to be adequate in the context of financial problems. In this study, we present the methodology used and the results obtained in the elaboration of a decision-support system for credit assignment. The problem was to provide an automatic tool for a Spanish financial institution that needed to quantify and analyse credit applications from clients. Firstly, we shall...

Rough modeling - a bottom-up approach to model construction

Terje Loken, Jan Komorowski (2001)

International Journal of Applied Mathematics and Computer Science

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Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen obj-ects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality-it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive emphand descriptive qualities, in...