skip to content
Handling missing data in ranked set sampling Preview this item
ClosePreview this item
Checking...

Handling missing data in ranked set sampling

Author: Carlos Narciso Bouza Herrera
Publisher: Heidelberg : Springer, 2013.
Series: SpringerBriefs in statistics
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
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.  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

 

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Carlos Narciso Bouza Herrera
ISBN: 9783642398995 3642398995
OCLC Number: 861744903
Description: 1 online resource (x, 116 pages).
Contents: 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.
Series Title: SpringerBriefs in statistics
Responsibility: Carlos N. Bouza-Herrera.
More information:

Abstract:

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.

Reviews

Editorial reviews

Publisher Synopsis

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. Read more...

 
User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


<http://www.worldcat.org/oclc/861744903>
library:oclcnum"861744903"
library:placeOfPublication
owl:sameAs<info:oclcnum/861744903>
rdf:typeschema:Book
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:about
schema:bookFormatschema:EBook
schema:creator
schema:datePublished"2013"
schema:description"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."@en
schema:exampleOfWork<http://worldcat.org/entity/work/id/1784230739>
schema:genre"Electronic books."@en
schema:inLanguage"en"
schema:name"Handling missing data in ranked set sampling"@en
schema:url
schema:url<http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=647456>
schema:url
schema:workExample
schema:workExample

Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.