000 03254nam a22004697a 4500
001 sulb-eb0025380
003 BD-SySUS
005 20160413122523.0
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008 131004s2013 gw | s |||| 0|eng d
020 _a9783642398995
_9978-3-642-39899-5
024 7 _a10.1007/978-3-642-39899-5
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aBouza-Herrera, Carlos N.
_eauthor.
245 1 0 _aHandling Missing Data in Ranked Set Sampling
_h[electronic resource] /
_cby Carlos N. Bouza-Herrera.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aX, 116 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Statistics,
_x2191-544X
505 0 _aPreface -- 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.
520 _aThe 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.
650 0 _aStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642398988
830 0 _aSpringerBriefs in Statistics,
_x2191-544X
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-39899-5
912 _aZDB-2-SMA
942 _2Dewey Decimal Classification
_ceBooks
999 _c47472
_d47472