000 | 03254nam a22004697a 4500 | ||
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001 | sulb-eb0025380 | ||
003 | BD-SySUS | ||
005 | 20160413122523.0 | ||
007 | cr nn 008mamaa | ||
008 | 131004s2013 gw | s |||| 0|eng d | ||
020 |
_a9783642398995 _9978-3-642-39899-5 |
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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. |
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300 |
_aX, 116 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Statistics, _x2191-544X |
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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 |
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999 |
_c47472 _d47472 |