Handling Missing Data in Ranked Set Sampling

Name: Handling Missing Data in Ranked Set Sampling
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783642398988
Price: 52.62 EUR
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Автор: Carlos N. Bouza-Herrera

Език:

Корици: С меки корици

Издател: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Наличност: Външен склад

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The existence of missing observations is a very important aspect to be considered in the application...

Информация за книгата

Автор

Carlos N. Bouza-Herrera

Език

Английски език

Корици

Книга - С меки корици

Издадена

2013

страници

116

EAN

9783642398988

ISBN

3642398987

Enbook ID

02081939

Издател

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Теглоt

2058

Размери

155 x 235 x 236

Пълно описание

The existence of missing observations is a very important aspect to be considered in the application of survey sampling, for example. In human populations they may be caused by a refusal of some interviewees to give the true value for the variable of interest. Traditionally, simple random sampling is used to select samples. Most statistical models are supported by the use of samples selected by means of this design. In recent decades, an alternative design has started being used, which, in many cases, shows an improvement in terms of accuracy compared with traditional sampling. It is called Ranked Set Sampling (RSS). A random selection is made with the replacement of samples, which are ordered (ranked). The literature on the subject is increasing due to the potentialities of RSS for deriving more effective alternatives to well-established statistical models. In this work, the use of RSS sub-sampling for obtaining information among the non respondents and different imputation procedures are considered. RSS models are developed as counterparts of well-known simple random sampling (SRS) models. SRS and RSS models for estimating the population using missing data are presented and compared both theoretically and using numerical experiments.

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Handling Missing Data in Ranked Set Sampling

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