國立虎尾科技大學 |

Evaluation of Statistical Matching and Selected SAE Methods = Using Micro Census and EU-SILC Data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Evaluation of Statistical Matching and Selected SAE Methods/ by Verena Puchner.
其他題名:	Using Micro Census and EU-SILC Data /
作者:	Puchner, Verena.
面頁冊數:	XIII, 101 p. 6 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computer mathematics. -
電子資源:	https://doi.org/10.1007/978-3-658-08224-6
ISBN:	9783658082246

Evaluation of Statistical Matching and Selected SAE Methods = Using Micro Census and EU-SILC Data /
Puchner, Verena.

Evaluation of Statistical Matching and Selected SAE MethodsUsing Micro Census and EU-SILC Data /[electronic resource] :by Verena Puchner. - 1st ed. 2015. - XIII, 101 p. 6 illus.online resource. - BestMasters,2625-3577. - BestMasters,.

Regression Models Including Selected Small Area Methods -- Statistical Matching -- Application to Poverty Estimation Using EU-SILC and Micro Census Data -- Bootstrap Methods.

Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census. Contents Regression Models Including Selected Small Area Methods Statistical Matching Application to Poverty Estimation Using EU-SILC and Micro Census Data Bootstrap Methods Target Groups Researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics The Author Verena Puchner obtained her master’s degree at Technical University of Vienna under the supervision of Priv.-Doz. Dipl.-Ing. Dr. techn. Matthias Templ. At present, she works as a data miner and consultant.

ISBN: 9783658082246

Standard No.: 10.1007/978-3-658-08224-6doiSubjects--Topical Terms:

1199796
Computer mathematics.

LC Class. No.: QA71-90

Dewey Class. No.: 518

Evaluation of Statistical Matching and Selected SAE Methods = Using Micro Census and EU-SILC Data /
LDR:02998nam a22003975i 4500 001 966588
003 DE-He213
005 20200920064054.0
007 cr nn 008mamaa
008 201211s2015 gw | s |||| 0|eng d
020 $a 9783658082246 $9 978-3-658-08224-6
024 7 $a 10.1007/978-3-658-08224-6 $2 doi
035 $a 978-3-658-08224-6
050 4 $a QA71-90
072 7 $a PBKS $2 bicssc
072 7 $a MAT006000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 518 $2 23
100 1 $a Puchner, Verena. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1063718
245 1 0 $a Evaluation of Statistical Matching and Selected SAE Methods $h [electronic resource] : $b Using Micro Census and EU-SILC Data / $c by Verena Puchner.
250 $a 1st ed. 2015.
264 1 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Spektrum, $c 2015.
300 $a XIII, 101 p. 6 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a BestMasters, $x 2625-3577
505 0 $a Regression Models Including Selected Small Area Methods -- Statistical Matching -- Application to Poverty Estimation Using EU-SILC and Micro Census Data -- Bootstrap Methods.
520 $a Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census. Contents Regression Models Including Selected Small Area Methods Statistical Matching Application to Poverty Estimation Using EU-SILC and Micro Census Data Bootstrap Methods Target Groups Researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics The Author Verena Puchner obtained her master’s degree at Technical University of Vienna under the supervision of Priv.-Doz. Dipl.-Ing. Dr. techn. Matthias Templ. At present, she works as a data miner and consultant.
650 0 $a Computer mathematics. $3 1199796
650 0 $a Probabilities. $3 527847
650 0 $a Applied mathematics. $3 1069907
650 0 $a Engineering mathematics. $3 562757
650 1 4 $a Computational Mathematics and Numerical Analysis. $3 669338
650 2 4 $a Probability Theory and Stochastic Processes. $3 593945
650 2 4 $a Applications of Mathematics. $3 669175
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783658082253
776 0 8 $i Printed edition: $z 9783658082239
830 0 $a BestMasters, $x 2625-3577 $3 1253531
856 4 0 $u https://doi.org/10.1007/978-3-658-08224-6
912 $a ZDB-2-BHS
912 $a ZDB-2-SXBP
950 $a Behavioral Science (SpringerNature-11640)
950 $a Behavioral Science and Psychology (R0) (SpringerNature-43718)