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＜電子ブック＞
Handling Missing Data in Ranked Set Sampling / by Carlos N. Bouza-Herrera
(SpringerBriefs in Statistics. ISSN:21915458)

版	1st ed. 2013.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2013
本文言語	英語
大きさ	X, 116 p : online resource
著者標目	*Bouza-Herrera, Carlos N author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry LCSH:Social sciences -- Statistical methods  全ての件名で検索 FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	Preface -- Missing Observations and Data Quality Improvement -- Sampling Using Ranked Sets: Basic Concepts -- The Non Response  Problem: Sub-sampling among the Non Respondents -- Imputation of the Missing Data -- Some Numerical Studies of the Behavior of RSS 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 HTTP:URL=https://doi.org/10.1007/978-3-642-39899-5

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