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Statistical Context (statistical + context)

Distribution by Scientific Domains

Information Science and Computing	50%

Selected Abstracts

CONTEXTUALIZING LEARNING OBJECTS USING ONTOLOGIES

COMPUTATIONAL INTELLIGENCE, Issue 3 2007
Phaedra Mohammed
Educational research over the past three years has intensified such that the context of learning resources needs to be properly modeled. Many researchers have described and even mandated the use of ontologies in the research being conducted, yet the process of actually connecting one or more ontologies to a learning object has not been extensively discussed. This paper describes a practical model for associating multiple ontologies with learning objects while making full use of the IEEE LOM specification. The model categorizes these ontologies according to five major categories of context based on the most popular fields of study actively being pursued by the educational research community: Thematic context, Pedagogical context, Learner context, Organizational context, and Historical/Statistical context. [source]

Optimal fuzzy reasoning and its robustness analysis

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2004
Lei Zhang
Fuzzy reasoning methods are extensively used in intelligent systems and fuzzy control. Most existing fuzzy reasoning methods follow rules of logical inference. In this article, fuzzy reasoning is treated as an optimization problem. The idea of optimal fuzzy reasoning is reviewed and three new optimal fuzzy reasoning methods are given by using new optimization objective functions. The robustness of fuzzy reasoning, that is, how errors in premises affect conclusions in fuzzy reasoning, is evaluated in a probabilistic or statistical context by using the Monte Carlo simulation method. Six optimal fuzzy reasoning methods are evaluated in comparison with the CRI method in terms of probabilistic robustness. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1033,1049, 2004. [source]

OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification,

JOURNAL OF CHEMOMETRICS, Issue 8-10 2006
Max Bylesjö
Abstract The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results. Copyright © 2007 John Wiley & Sons, Ltd. [source]

The Concordance Index C and the Mann,Whitney Parameter Pr(X>Y) with Randomly Censored Data

BIOMETRICAL JOURNAL, Issue 3 2009
James A. Koziol
Abstract Harrell's c -index or concordance C has been widely used as a measure of separation of two survival distributions. In the absence of censored data, the c -index estimates the Mann,Whitney parameter Pr(X>Y), which has been repeatedly utilized in various statistical contexts. In the presence of randomly censored data, the c -index no longer estimates Pr(X>Y); rather, a parameter that involves the underlying censoring distributions. This is in contrast to Efron's maximum likelihood estimator of the Mann,Whitney parameter, which is recommended in the setting of random censorship. [source]