Imputation methods to handle the problem of missing data: an application using R/Splus

Authors

  • Juan Francisco Muñoz Rosas Departamento de Métodos Cuantitativos para la Economía y la Empresa Universidad de Granada
  • Encarnación Álvarez Verdejo Departamento de Métodos Cuantitativos para la Economía y la Empresa Universidad de Granada

DOI:

https://doi.org/10.46661/revmetodoscuanteconempresa.2120

Keywords:

Información auxiliar, encuesta, probabilidades de inclusión, mecanismo de respuesta, auxiliary information, survey, inclusion probabilities, response mechanism

Abstract

Missing values are a common problem in many sampling surveys, and imputation is usually employed to compensate for non-response. Most imputation methods are based upon the problem of the mean estimation and its variance, and they also assume simple sampling designs such as the simple random sampling without replacement. In this paper we describe some imputation methods and define them under a general sampling design. Different response mechanisms are also discussed. Assuming some populations based upon real data extracted from the context of the economy and business, Monte Carlo simulations are carried out to analyze the properties of the various imputation methods in the estimation of parameters such as distribution functions and quantiles. The various imputation methods are implemented using the popular statistical softwares R and Splus, and codes are here presented.

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Published

2016-11-04

How to Cite

Muñoz Rosas, J. F., & Álvarez Verdejo, E. (2016). Imputation methods to handle the problem of missing data: an application using R/Splus. Journal of Quantitative Methods for Economics and Business Administration, 7, Páginas 3 a 30. https://doi.org/10.46661/revmetodoscuanteconempresa.2120

Issue

Vol. 7 (2009)

Section

Articles

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