Descriptive Statistics for Interval-valued Observations in the presence of Rules
Billard, Lynne; Diday, Edwin (2006), Descriptive Statistics for Interval-valued Observations in the presence of Rules, Computational Statistics, 21, 2, p. 187-210. http://dx.doi.org/10.1007/s00180-006-0259-6
TypeArticle accepté pour publication ou publié
Journal nameComputational Statistics
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Abstract (EN)While symbolic data exist in their own right, contemporary datasets can be too large to analyse using traditional statistical methodologies. Aggregation of these large datasets into sets of more managable size perforce produce datasets whose entries are symbolic data. This paper studies the derivation of basic description statistics, in particular, histograms and mean and variances plus joint histograms for interval-valued datasets when logical dependency rules are present. Algorithms for calculating these histograms are also provided.
Subjects / KeywordsInterval-valued data ; logical dependency rules ; univariate histogrames ; sample means ; sample variances ; joint histogram
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Classification and Regression Trees on Aggregate Data Modeling: An Application in Acute Myocardial Infarction Quantin, Catherine; Billard, Lynne; Touati, Myriam; Andreu, N.; Cotin, Y.; Zeller, Manfred; Afonso, Filipe; Battaglia, G.; Seck, Djamal; Le Teuff, G.; Diday, Edwin (2011) Article accepté pour publication ou publié