By Verena Puchner (auth.)

ISBN-10: 3658082232

ISBN-13: 9783658082239

ISBN-10: 3658082240

ISBN-13: 9783658082246

Verena Puchner evaluates and compares statistical matching and chosen SAE tools. given that poverty estimation at neighborhood point according to EU-SILC samples isn't really of enough accuracy, the standard of the estimations might be greater by means of also incorporating micro census information. the purpose is to discover the simplest procedure for the estimation of poverty by way of small bias and small variance by using a simulated man made "close-to-reality" inhabitants. Variables of curiosity are imputed into the micro census facts units with the aid of the EU-SILC samples via regression types together with chosen unit-level small quarter tools and statistical matching equipment. Poverty symptoms are then anticipated. the writer evaluates and compares the prejudice and variance for the direct estimator and a few of the equipment. The variance is wanted to be decreased by way of the bigger pattern dimension of the micro census.

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**Download PDF by Verena Puchner (auth.): Evaluation of Statistical Matching and Selected SAE Methods:**

Verena Puchner evaluates and compares statistical matching and chosen SAE tools. in view that poverty estimation at local point in line with EU-SILC samples isn't of sufficient accuracy, the standard of the estimations could be greater by way of also incorporating micro census information. the purpose is to discover the easiest strategy for the estimation of poverty by way of small bias and small variance by using a simulated man made "close-to-reality" inhabitants.

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For example, in 2010 the sample involved 14085 persons in 6188 households. The deepest used regional stratiﬁcation is NUTS 2. NUTS is a common classiﬁcation of territorial units for statistics based on Regulation 1059/2003 of the European Parliament and of the Council. The level NUTS 2 corresponds to the 9 states of Austria. The sample sizes for the several states are diﬀerent (proportional to the inhabitants), therefore it is approximately a self-weighting design. All persons of a household aged 16 years or older get interviewed personally and furthermore some information on children gets collected.

It appears that now the logistic mixed regression model (e)(ii) performs best among the regression models, followed by the logistic regression model (c)(ii), the logistic mixed regression model (e)(i) and then the logistic regression model (c)(i). 2, Table 32): Now again the logistic mixed regression model (e)(ii) performs best, followed by the logistic regression model (c)(ii), then by the logistic regression model (c)(i) and the logistic mixed regression model (e)(i). 44 After careful analysis it seems as if there is a “regression to the middle”, so that the states with a higher at-risk-of-poverty rate, as for example Vienna or Carinthia, tend to get a lower rate and the states with a lower at-risk-of-poverty rate, as for example Lower Austria or Upper Austria, tend to get a higher rate.

102556 DMS (Eq. 093183 Table 5: Diﬀerence in the mean of the at-risk-of-poverty rates from the bootstrap replicates (of all drawn samples) and the mean of the at-risk-of-poverty rates calculated on the basis of the results of the repeated EU-SILC sample drawing [in %]. (h)(ii), random hot deck model (f)(iv), weighted random hot deck model (h)(iv), random hot deck model (f)(ii), random hot deck model (f)(i), sequential random hot deck model (g)(ii), sequential random hot deck model (g)(i), logistic regression model (c)(ii), logistic mixed regression model (e)(ii), logistic mixed regression model (e)(i), logistic regression model (c)(i).

### Evaluation of Statistical Matching and Selected SAE Methods: Using Micro Census and EU-SILC Data by Verena Puchner (auth.)

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