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… Mehr…
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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… Mehr…
ISBN: 9783642398995
; EPUB; Reference & Languages > Sociology & anthropology > Sociology > Social research & statistics, Penguin Books Ltd
2013
ISBN: 9783642398995
eBooks, eBook Download (PDF), Auflage, [PU: Springer-Verlag], [ED: 1], Springer-Verlag, 2013
2013, ISBN: 9783642398995
eBooks, eBook Download (PDF), 2013, [PU: Springer Berlin], Springer Berlin, 2013
2013, ISBN: 9783642398995
2013, eBook Download (PDF), eBooks, [PU: Springer Berlin]
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Detailangaben zum Buch - Handling Missing Data in Ranked Set Sampling
EAN (ISBN-13): 9783642398995
ISBN (ISBN-10): 3642398995
Erscheinungsjahr: 2013
Herausgeber: Springer-Verlag
116 Seiten
Sprache: eng/Englisch
Buch in der Datenbank seit 2012-05-06T20:02:49+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-03-04T03:39:23+01:00 (Berlin)
ISBN/EAN: 9783642398995
ISBN - alternative Schreibweisen:
3-642-39899-5, 978-3-642-39899-5
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: herre, herrera, bou, pattanaik
Titel des Buches: missing, data, ranke
Daten vom Verlag:
Autor/in: Carlos N. Bouza-Herrera
Titel: SpringerBriefs in Statistics; Handling Missing Data in Ranked Set Sampling
Verlag: Springer; Springer Berlin
116 Seiten
Erscheinungsjahr: 2013-10-04
Berlin; Heidelberg; DE
Sprache: Englisch
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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