Last edited by Springer
04.08.2021 | History

1 edition of Handling Missing Data in Ranked Set Sampling found in the catalog.

Handling Missing Data in Ranked Set Sampling

successfully teaching sports to every child

  • 212 Want to read
  • 728 Currently reading

Published by Administrator in Springer

    Places:
  • United States
    • Subjects:
    • Springer


      • Download Handling Missing Data in Ranked Set Sampling Book Epub or Pdf Free, Handling Missing Data in Ranked Set Sampling, Online Books Download Handling Missing Data in Ranked Set Sampling Free, Book Free Reading Handling Missing Data in Ranked Set Sampling Online, You are free and without need to spend extra money (PDF, epub) format You can Download this book here. Click on the download link below to get Handling Missing Data in Ranked Set Sampling book in PDF or epub free.

      • Source title: Handling Missing Data in Ranked Set Sampling (SpringerBriefs in Statistics)

        StatementSpringer
        PublishersSpringer
        Classifications
        LC ClassificationsOct 05, 2013
        The Physical Object
        Paginationxvi, 121 p. :
        Number of Pages53
        ID Numbers
        ISBN 103642398987
        Series
        1nodata
        2
        3

        nodata File Size: 8MB.


Share this book
You might also like

Handling Missing Data in Ranked Set Sampling by Springer Download PDF EPUB FB2


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. The literature on the subject is increasing due to the potentialities of RSS for deriving more effective alternatives to well-established statistical models.

Handling Missing Data in Ranked Set Sampling

RSS models are developed as counterparts of well-known simple random sampling SRS models. It is called Ranked Set Sampling RSS. In human populations they may be caused by a refusal of some interviewees to give the true value for the variable of interest. In this work, the use of RSS sub-sampling for obtaining information among the non respondents and different imputation procedures are considered.

Handling Missing Data in Ranked Set Sampling

SRS and RSS models for estimating the population using missing data are presented and compared both theoretically and using numerical experiments. 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. A random selection is made with the replacement of samples, which are ordered ranked.