Summary
The problem of missing data is often addressed with imputation. Traditional single imputation methods, such as the ratio imputation, multiple regression imputation, nearest neighbor imputation, respondent mean imputation or hot deck imputation, have been widely used to compensate for non-response. Nonparametric regression methods have been recently applied to the estimation of the regression function in a wide range of settings and areas of research. The focus of this work is on replacing missing observations on a variable of interest by imputed values obtained from a new algorithm based on Multivariate Adaptive Regression Splines. Some imputation methods can lead to serious underestimation for measures of population distributions. This bias can be reduced by adding to the imputed values a small disturbance drawn from a given distribution. Two different methods of adding the random disturbance are also described. Numerical examples are presented to illustrate the theoretical results and analyze the precision of the proposed method.
The authors would like to thank the editors for this opportunity to contribute to this volume in honour to María Luisa Menéndez.
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Sánchez-Borrego, I., del Mar Rueda, M., Muñoz, J.F. (2011). Imputation and Inference with Multivariate Adaptive Regression Splines. In: Pardo, L., Balakrishnan, N., Gil, M.Á. (eds) Modern Mathematical Tools and Techniques in Capturing Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20853-9_24
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DOI: https://doi.org/10.1007/978-3-642-20853-9_24
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