Titlebook: Handling Missing Data in Ranked Set Sampling; Carlos N. Bouza-Herrera Book 2013 The Author(s) 2013 62D05, 62F05, 62F10, 62Pxx, 62F40.estim

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ch directed towards the technical development of materials that were strongly absorbing at short wavelengths (visible light) while being highly reflective at longer wavelengths (infrared radiation) extends back at least fifty years. But it is generally agreed that the modern development of selective

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Sampling Using Ranked Sets: Basic Concepts,ected by means of this design. During the last decade Ranked Set Sampling has appeared as a challenge to this design. It is implemented by selecting units with replacement and the sampled units are ordered (ranked). Each order statistic is observed once. This process can be repeated if needed to obs

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The Non-response Problem: Subsampling Among the Non-respondents,nding is high because the non-responses may be generated by the existence of a very different behavior of a group of units. This is especially important when human populations are sampled. The solution of subsampling among the non-respondents is the most intelligent approach in such cases. The usual

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Some Numerical Studies of the Behavior of RSS,atives. The expressions support general evaluations of the gains in accuracy but their values depend on the underlying distribution or the characteristics of the studied population. We present some numerical studies for illustrating the behavior of RSS strategies.

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https://doi.org/10.1007/978-3-642-39899-562D05, 62F05, 62F10, 62Pxx, 62F40; estimation of the population mean; imputation of missing observatio

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