Applications of the random recursive partitioning (RRP) method are described. This method generates a proximity matrix which can be used in non-parametric matching problems such as hot-deck missing data imputation and average treatment effect estimation. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partitions of the data and calculates the proximity between observations as the empirical frequency in the same cell of these random partitions over all the replications. Also, the method in the presence of missing data is invariant under monotonic transformations of the data but no other formal properties of the method are known yet. Therefore, Monte Carlo experiments were conducted in order to explore the performance of the method. A companion software is available as a package for the R statistical environment. (C) 2007 Elsevier B.V. All rights reserved

Missing data imputation, matching and other applications of random recursive partitioning / S.M. Iacus, G. Porro. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - 52:2(2007), pp. 773-789.

Missing data imputation, matching and other applications of random recursive partitioning

S.M. Iacus
Primo
;
2007

Abstract

Applications of the random recursive partitioning (RRP) method are described. This method generates a proximity matrix which can be used in non-parametric matching problems such as hot-deck missing data imputation and average treatment effect estimation. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partitions of the data and calculates the proximity between observations as the empirical frequency in the same cell of these random partitions over all the replications. Also, the method in the presence of missing data is invariant under monotonic transformations of the data but no other formal properties of the method are known yet. Therefore, Monte Carlo experiments were conducted in order to explore the performance of the method. A companion software is available as a package for the R statistical environment. (C) 2007 Elsevier B.V. All rights reserved
Average treatment effect estimation; Classification; Missing data imputation; Recursive partitioning
Settore MAT/06 - Probabilita' e Statistica Matematica
2007
Article (author)
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/53098
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 32
  • ???jsp.display-item.citation.isi??? 25
social impact