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KriMI: A Multiple Imputation Approach for Preserving Spatial Dependencies - Imputation of Regional Price Indices using the Example of Bavaria

English · Paperback / Softback

Description

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Multiple imputation is a method to handle the problem of missing values in a dataset. As it accounts for the uncertainty brought in by the missing data, it is possible to conduct reliable statistical tests after this method has been implemented.

Kriging uses neighbourhood effects to predict values of unobserved regions. It can be seen as an imputation technique. The unobserved regions are missing data points, and the kriging predictions are the imputations. Due to the fact of being a single imputation technique, no proper statistical inferences are possible after filling the dataset.

If spatially dependent data face the problem of missing data and a proper statistical inference is needed, a modelling of the spatial correlation in the multiple imputation model is needed. Here this is prevailed by implementing kriging in the model used for multiple imputation. We call the resulting method KriMI.

The exact problem can be found when looking at regional price levels in Bavaria. The Bavarian State Office for Statistics surveys the prices which are needed to compute the price index only in a few regions. The prices of the unobserved regions are treated as missing data.

Product details

Authors Sara Bleninger
Publisher Otto-Friedrich-Uni
 
Languages English
Product format Paperback / Softback
Released 30.04.2018
 
No. of pages 270
Dimensions 148 mm x 211 mm x 17 mm
Weight 435 g
Illustrations graph. Darst., Kt.
Series Schriften aus der Fakultät Sozial- und Wirtschaftswissenschaften
Schriften aus der Fakultät Sozial- und Wirtschaftswissenschaften
Subjects Social sciences, law, business > Social sciences (general)

Bayern, Bayern / Wirtschaft, Verkehr, Bayern; Fehlende Daten; Kriging; Preisindex; Räumliche Statistik

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