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Handling Missing Data in Ranked Set Sampling
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Handling Missing Data in Ranked Set Sampling - nouveau livre

2013, ISBN: 9783642398995

¿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… Plus…

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Handling Missing Data in Ranked Set Sampling - DEVDUTT PATTANAIK
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Handling Missing Data in Ranked Set Sampling - nouveau livre

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Handling Missing Data in Ranked Set Sampling - Carlos N Bouza-Herrera
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Carlos N Bouza-Herrera:
Handling Missing Data in Ranked Set Sampling - Première édition

2013

ISBN: 9783642398995

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Handling Missing Data in Ranked Set Sampling - Carlos N. Bouza-Herrera
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Carlos N. Bouza-Herrera:
Handling Missing Data in Ranked Set Sampling - nouveau livre

2013, ISBN: 9783642398995

eBooks, eBook Download (PDF), 2013, [PU: Springer Berlin], Springer Berlin, 2013

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Handling Missing Data in Ranked Set Sampling - Carlos N. Bouza-Herrera
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Carlos N. Bouza-Herrera:
Handling Missing Data in Ranked Set Sampling - nouveau livre

2013, ISBN: 9783642398995

2013, eBook Download (PDF), eBooks, [PU: Springer Berlin]

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Handling Missing Data in Ranked Set Sampling
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9783642398995

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EAN (ISBN-13): 9783642398995
ISBN (ISBN-10): 3642398995
Date de parution: 2013
Editeur: Springer-Verlag
116 Pages
Langue: eng/Englisch

Livre dans la base de données depuis 2012-05-06T20:02:49+02:00 (Zurich)
Page de détail modifiée en dernier sur 2023-03-04T03:39:23+01:00 (Zurich)
ISBN/EAN: 9783642398995

ISBN - Autres types d'écriture:
3-642-39899-5, 978-3-642-39899-5
Autres types d'écriture et termes associés:
Auteur du livre: herre, herrera, bou, pattanaik
Titre du livre: missing, data, ranke

Données de l'éditeur

Auteur: Carlos N. Bouza-Herrera
Titre: SpringerBriefs in Statistics; Handling Missing Data in Ranked Set Sampling
Editeur: Springer; Springer Berlin
116 Pages
Date de parution: 2013-10-04
Berlin; Heidelberg; DE
Langue: Anglais
53,49 € (DE)
55,00 € (AT)
67,00 CHF (CH)
Available
X, 116 p.

EA; E107; eBook; Nonbooks, PBS / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; 62D05, 62F05, 62F10, 62Pxx, 62F40; estimation of the population mean; imputation of missing observations; missing data; ranked set sampling; subsampling the non response stratum; C; Statistical Theory and Methods; Biostatistics; Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy; Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematics and Statistics; Sozialforschung und -statistik; BC

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.
Fills the gap in the literature on missing observations for ranked set sampling models Provides ready-to-use models for dealing with non responses in surveys Prepares the reader to develop further research on estimation with missing observations? Includes supplementary material: sn.pub/extras

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