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Binary trees for dissimilarity data

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  • Piccarreta, Raffaella

Abstract

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.

Suggested Citation

  • Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1516-1524
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    References listed on IDEAS

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    1. David R. Larsen & Paul L. Speckman, 2004. "Multivariate Regression Trees for Analysis of Abundance Data," Biometrics, The International Biometric Society, vol. 60(2), pages 543-549, June.
    2. 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.
    3. 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.
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    5. Dine, Abdessamad & Larocque, Denis & Bellavance, François, 2009. "Multivariate trees for mixed outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3795-3804, September.
    6. 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.
    7. Pierpaolo D’Urso, 2000. "Dissimilarity measures for time trajectories," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 9(1), pages 53-83, January.
    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, October.
    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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    Cited by:

    1. Antonella Plaia & Mariangela Sciandra, 2019. "Weighted distance-based trees for ranking data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 427-444, June.
    2. Matthias Studer & Gilbert Ritschard & Alexis Gabadinho & Nicolas S. Müller, 2011. "Discrepancy Analysis of State Sequences," Sociological Methods & Research, , vol. 40(3), pages 471-510, August.
    3. Marco Bonetti & Raffaella Piccarreta & Gaia Salford, 2013. "Parametric and Nonparametric Analysis of Life Courses: An Application to Family Formation Patterns," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 881-902, June.
    4. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.

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