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Handling Missing Data in Ranked Set Sampling pp 59–90Cite as

Imputation of the Missing Data

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Abstract

We may consider the existence of missing observations as unimportant, considering that the risk of misunderstanding is negligible. The surveyor assumes some model that allows adequately explaining the variable of interest. In such cases, we are able to predict the unknown values and to plug them into some estimator. Generally, the models used for imputing in sampling are not complicated and rely on simple ideas. Imputation in simple random sampling has been developed for decades; the literature is increased yearly. Ranked Set Sampling (RSS) alternatives are presented in this chapter. The efficiency of this approach is supported for the different models. On some occasions the preference of RSS is doubtful and needs numerical comparisons.

Let us act on what we have, since we have not what we wish.

Cardinal Newman

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Authors and Affiliations

  1. Facultad de Matemática y, Computaciòn, Universidad de La Habana, San Lázaro y L., Havana, 10400, Cuba

    Carlos N. Bouza-Herrera

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  1. Carlos N. Bouza-Herrera
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Correspondence to Carlos N. Bouza-Herrera .

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Bouza-Herrera, C.N. (2013). Imputation of the Missing Data. In: Handling Missing Data in Ranked Set Sampling. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39899-5_4

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