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Bootstrap and Nonparametric Predictors to Impute Missing Data

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Book cover Classification and Multivariate Analysis for Complex Data Structures

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Abstract

A new nonparametric technique to impute missing data is proposed in order to obtain a completed data-matrix, capable of producing a degree of reliability for the imputations. Without taking into account strong assumptions, we introduce multiple imputations using bootstrap and nonparametric predictors. It is shown that, in this manner, we can obtain better imputations than with other known methods producing a more reliable completed data-matrix. Using two simulations, we show that the proposed technique can be generalized to consider non-monotone patterns of missing data with interesting results.

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Author information

Authors and Affiliations

  1. Department of Statistics, Probability and Applied Statistics, Sapienza University of Rome, Rome, Italy

    Agostino Di Ciaccio

Authors
  1. Agostino Di Ciaccio
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Correspondence to Agostino Di Ciaccio .

Editor information

Editors and Affiliations

  1. , Laboratoire d' Informatique, Université d' Aix-Marseille II, Avenue de Luminy 163case 901, Marseille cedex 9, 13288, France

    Bernard Fichet

  2. , Dipartimento di Scienze Statistiche, Università di Napoli "Federico II", Via Leopoldo Rodinò 22, Naples, 80138, Italy

    Domenico Piccolo

  3. , Facoltà di Studi Politici "Jean Monnet", Seconda Università di Napoli, Via del Setificio 15, Caserta, 81100, Italy

    Rosanna Verde

  4. , Facoltà di Scienze Statistiche, Università di Roma "La Sapienza", P.le Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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Di Ciaccio, A. (2011). Bootstrap and Nonparametric Predictors to Impute Missing Data. In: Fichet, B., Piccolo, D., Verde, R., Vichi, M. (eds) Classification and Multivariate Analysis for Complex Data Structures. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13312-1_20

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