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Handling missing data in ranked set sampling

Autor: Carlos Narciso Bouza Herrera
Editora: Heidelberg : Springer, 2013.
Séries: SpringerBriefs in statistics
Edição/Formato   e-book : Documento : InglêsVer todas as edições e formatos
Base de Dados:WorldCat
Resumo:
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.  Ler mais...
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Detalhes

Gênero/Forma: Electronic books
Formato Físico Adicional: Print version:
Bouza-Herrera, Carlos N.
Handling Missing Data in Ranked Set Sampling.
Dordrecht : Springer, ©2013
Tipo de Material: Documento, Recurso Internet
Tipo de Documento: Recurso Internet, Arquivo de Computador
Todos os Autores / Contribuintes: Carlos Narciso Bouza Herrera
ISBN: 9783642398995 3642398995 3642398987 9783642398988
Número OCLC: 861744903
Descrição: 1 online resource (x, 116 pages).
Conteúdos: Missing observations and data quality improvement --
Sampling using ranked sets: basic concepts --
The non-response problem: subsampling among the non-respondents --
Imputation of the missing data --
Some numerical studies of the behavior of RSS.
Título da Série: SpringerBriefs in statistics
Responsabilidade: Carlos N. Bouza-Herrera.

Resumo:

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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From the reviews: "This monograph treats missing data due to non-inclusion of units in the sampling frame (non-coverage) or to individual non-responses in theoretical 'ranked set sampling' framework. Ler mais...

 
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