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Binary trees for dissimilarity data

Listed author(s):
  • Piccarreta, Raffaella
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    Binary segmentation procedures (in particular, classification and regression trees) are extended to study the relation between dissimilarity data and a set of explanatory variables. The proposed split criterion is very flexible, and can be applied to a wide range of data (e.g., mixed types of multiple responses, longitudinal data, sequence data). Also, it can be shown to be an extension of well-established criteria introduced in the literature on binary trees.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 54 (2010)
    Issue (Month): 6 (June)
    Pages: 1516-1524

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    Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1516-1524
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    1. Cees H. Elzinga, 2005. "Combinatorial Representations of Token Sequences," Journal of Classification, Springer;The Classification Society, vol. 22(1), pages 87-118, June.
    2. Dine, Abdessamad & Larocque, Denis & Bellavance, François, 2009. "Multivariate trees for mixed outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3795-3804, September.
    3. Siciliano, Roberta & Mola, Francesco, 2000. "Multivariate data analysis and modeling through classification and regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 285-301, January.
    4. Pierpaolo D’Urso, 2000. "Dissimilarity measures for time trajectories," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 9(1), pages 53-83, January.
    5. Sexton, Joseph & Laake, Petter, 2009. "Standard errors for bagged and random forest estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 801-811, January.
    6. Briand, Bénédicte & Ducharme, Gilles R. & Parache, Vanessa & Mercat-Rommens, Catherine, 2009. "A similarity measure to assess the stability of classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1208-1217, February.
    7. Duncan McVicar & Michael Anyadike-Danes, 2002. "Predicting successful and unsuccessful transitions from school to work by using sequence methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 317-334.
    8. Raffaella Piccarreta & Francesco C. Billari, 2007. "Clustering work and family trajectories by using a divisive algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1061-1078.
    9. Henk Kiers & Donatella Vicari & Maurizio Vichi, 2005. "Simultaneous classification and multidimensional scaling with external information," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 433-460, September.
    10. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
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