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Handling Missing Data in Ranked Set Sampling - Carlos N. Bouza-Herrera
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2013, ISBN: 9783642398995

ID: 9200000033968159

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 ..., 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.Taal: Engels; Formaat: ePub met kopieerbeveiliging (DRM) van Adobe; Kopieerrechten: Het kopiëren van (delen van) de pagina's is niet toegestaan ; Geschikt voor: Alle e-readers geschikt voor ebooks in ePub formaat. Tablet of smartphone voorzien van een app zoals de bol.com Kobo app.; Verschijningsdatum: oktober 2013; ISBN10: 3642398995; ISBN13: 9783642398995; , Engelstalig | Ebook | 2013, Studie & Management, Medisch & Gezondheid, Studie & Management, Mens & Maatschappij, Methodologie, Studie & Management, Exacte wetenschappen, Wiskunde & Statistiek, Springer

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Handling Missing Data in Ranked Set Sampling - Carlos N Bouza-Herrera
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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 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. Adult, Mathematics, Handling Missing Data in Ranked Set Sampling~~ Carlos N Bouza-Herrera~~Adult~~Mathematics~~9783642398995, en, Handling Missing Data in Ranked Set Sampling, Carlos N Bouza-Herrera, 9783642398995, Springer, 10/04/2013, , , , Springer, 10/04/2013

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Handling Missing Data in Ranked Set Sampling:SpringerBriefs in Statistics. 2013. Auflage Carlos N Bouza-Herrera Handling Missing Data in Ranked Set Sampling:SpringerBriefs in Statistics. 2013. Auflage Carlos N Bouza-Herrera eBooks > Fachthemen & Wissenschaft > Medizin, Springer Berlin Heidelberg

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SpringerBriefs in Statistics. 2013. Auflage SpringerBriefs in Statistics. 2013. Auflage eBooks > Fachthemen Wissenschaft > Medizin, [PU: Springer, Berlin/Heidelberg/New York, NY]

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Handling Missing Data in Ranked Set Sampling
Autor:

Bouza-Herrera, Carlos N

Titel:

Handling Missing Data in Ranked Set Sampling

ISBN-Nummer:

9783642398995

Detailangaben zum Buch - Handling Missing Data in Ranked Set Sampling


EAN (ISBN-13): 9783642398995
ISBN (ISBN-10): 3642398995
Erscheinungsjahr: 2013
Herausgeber: Springer Berlin
116 Seiten
Sprache: eng/Englisch

Buch in der Datenbank seit 06.05.2012 20:02:49
Buch zuletzt gefunden am 21.02.2017 18:11:18
ISBN/EAN: 9783642398995

ISBN - alternative Schreibweisen:
3-642-39899-5, 978-3-642-39899-5

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