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Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm

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  • Antonio D’Ambrosio
  • Massimo Aria
  • Roberta Siciliano

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  • Antonio D’Ambrosio & Massimo Aria & Roberta Siciliano, 2012. "Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 227-258, July.
    • Handle: RePEc:spr:jclass:v:29:y:2012:i:2:p:227-258
    DOI: 10.1007/s00357-012-9108-1
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    References listed on IDEAS

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    1. Saporta, Gilbert, 2002. "Data fusion and data grafting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 465-473, February.
    2. Cappelli, Carmela & Mola, Francesco & Siciliano, Roberta, 2002. "A statistical approach to growing a reliable honest tree," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 285-299, January.
    3. Claudio Conversano & Roberta Siciliano, 2009. "Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 361-379, December.
    4. Conti, Pier Luigi & Marella, Daniela & Scanu, Mauro, 2008. "Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 354-365, December.
    5. Gey, Servane & Poggi, Jean-Michel, 2006. "Boosting and instability for regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 533-550, January.
    6. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
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    Citations

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    Cited by:

    1. Ivan Miguel Pires & Faisal Hussain & Nuno M. Garcia & Eftim Zdravevski, 2020. "Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study," Future Internet, MDPI, vol. 12(9), pages 1-18, September.
    2. Roberta Siciliano & Antonio D’Ambrosio & Massimo Aria & Sonia Amodio, 2017. "Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 473-493, October.
    3. Amodio, S. & D’Ambrosio, A. & Siciliano, R., 2016. "Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach," European Journal of Operational Research, Elsevier, vol. 249(2), pages 667-676.
    4. Massimo Aria & Antonio D’Ambrosio & Carmela Iorio & Roberta Siciliano & Valentina Cozza, 2020. "Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images," Statistical Papers, Springer, vol. 61(4), pages 1645-1661, August.
    5. Lukasz Struski & Marek Śmieja & Jacek Tabor, 2020. "Pointed Subspace Approach to Incomplete Data," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 42-57, April.
    6. Zahra Rezaei Ghahroodi, 2023. "Statistical matching of sample survey data: application to integrate Iranian time use and labour force surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1023-1051, September.

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