@incollection {springerlink:10.1007/978-3-7908-2380-6_17,
author = {Köppen, Veit and Lenz, Hans-J.},
affiliation = {Freie Universität Berlin Institute of Production, Information Systems and Operations Research Garystr. 21 D-14195 Berlin Germany},
title = {Data Quality Control Based on Metric Data Models},
booktitle = {Frontiers in Statistical Quality Control 9},
editor = {Lenz, Hans-Joachim and Wilrich, Peter-Theodor and Schmid, Wolfgang},
publisher = {Physica-Verlag HD},
isbn = {978-3-7908-2380-6},
keyword = {Statistics},
pages = {263-276},
url = {http://dx.doi.org/10.1007/978-3-7908-2380-6_17},
note = {10.1007/978-3-7908-2380-6_17},
abstract = {We consider statistical edits defined on a metric data space spanned by the nonkey attributes (variables) of a given database. Integrity constraints are defined on this data space based on definitions, behavioral equations or a balance equation system. As an example think of a set of business or economic indicators. The variables are linked by the four basic arithmetic operations only. Assuming a multivariate Gaussian distribution and an error in the variables model estimation of the unknown (latent) variables can be carried out by a generalized least-squares (GLS) procedure. The drawback of this approach is that the equations form a non-linear equation system due to multiplication and division of variables, and that generally one assumes independence between all variables due to a lack of information in real applications. As there exists no finite parameter density family which is closed under all four arithmetic operations we use MCMC-simulation techniques, cf. Smith and Gelfand (1992) and Chib (2004) to derive the exact distributions in the non-normal case and under cross-correlation. The research can be viewed as an extension of Köppen and Lenz (2005) in the sense of studying the robustness of the GLS approach with respect to non-normality and correlation.},
year = {2010}
}