000 03009nam a22003735i 4500
001 308288
003 MX-SnUAN
005 20160429160208.0
007 cr nn 008mamaa
008 150903s2013 gw | o |||| 0|eng d
020 _a9783642398995
_99783642398995
024 7 _a10.1007/9783642398995
_2doi
035 _avtls000361886
039 9 _a201509030626
_bVLOAD
_c201405070321
_dVLOAD
_y201402211037
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aBouza-Herrera, Carlos N.
_eautor
_9348161
245 1 0 _aHandling Missing Data in Ranked Set Sampling /
_cby Carlos N. Bouza-Herrera.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _ax, 116 páginas
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringerBriefs in Statistics,
_x2191-544X
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783642398988
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-39899-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c308288
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