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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 - Taschenbuch

ISBN: 9783642398988

ID: 9783642398988

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. Handling Missing Data in Ranked Set Sampling: 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., Springer

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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 - Taschenbuch

2013, ISBN: 9783642398988

[ED: Taschenbuch], [PU: Springer], Neuware - 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., [SC: 0.00], Neuware, gewerbliches Angebot, 238x156x15 mm, [GW: 207g]

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Carl Hübscher GmbH
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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 - Taschenbuch

2013

ISBN: 9783642398988

[ED: Taschenbuch], [PU: Springer], Neuware - 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., [SC: 0.00], Neuware, gewerbliches Angebot, FixedPrice, [GW: 207g]

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Handling Missing Data in Ranked Set Sampling - Carlos N Bouza-Herrera
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
(*)
Carlos N Bouza-Herrera:
Handling Missing Data in Ranked Set Sampling - Taschenbuch

2013, ISBN: 9783642398988

[ED: Taschenbuch], [PU: Springer], Neuware - 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., [SC: 0.00]

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Handling Missing Data in Ranked Set Sampling - Carlos N Bouza-Herrera
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
(*)
Carlos N Bouza-Herrera:
Handling Missing Data in Ranked Set Sampling - Taschenbuch

ISBN: 9783642398988

[ED: Taschenbuch], [PU: Springer], Neuware - 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., [SC: 1.40]

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Handling Missing Data in Ranked Set Sampling
Autor:

Carlos N Bouza-Herrera

Titel:

Handling Missing Data in Ranked Set Sampling

ISBN-Nummer:

9783642398988

Detailangaben zum Buch - Handling Missing Data in Ranked Set Sampling


EAN (ISBN-13): 9783642398988
ISBN (ISBN-10): 3642398987
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2013
Herausgeber: Springer Berlin Heidelberg

Buch in der Datenbank seit 11.04.2014 17:31:16
Buch zuletzt gefunden am 18.02.2017 09:20:48
ISBN/EAN: 9783642398988

ISBN - alternative Schreibweisen:
3-642-39898-7, 978-3-642-39898-8

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