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Title: Asymptotics for weakly dependent errors-in-variables (English)
Author: Pešta, Michal
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 49
Issue: 5
Year: 2013
Pages: 692-704
Summary lang: English
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Category: math
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Summary: Linear relations, containing measurement errors in input and output data, are taken into account in this paper. Parameters of these so-called errors-in-variables (EIV) models can be estimated by minimizing the total least squares (TLS) of the input-output disturbances. Such an estimate is highly non-linear. Moreover in some realistic situations, the errors cannot be considered as independent by nature. Weakly dependent ($\alpha$- and $\varphi$-mixing) disturbances, which are not necessarily stationary nor identically distributed, are considered in the EIV model. Asymptotic normality of the TLS estimate is proved under some reasonable stochastic assumptions on the errors. Derived asymptotic properties provide necessary basis for the validity of block-bootstrap procedures. (English)
Keyword: errors-in-variables (EIV)
Keyword: dependent errors
Keyword: total least squares (TLS)
Keyword: asymptotic normality
MSC: 15A51
MSC: 15A52
MSC: 62E20
MSC: 62J05
MSC: 62J99
MSC: 65F15
idZBL: Zbl 1278.15036
idMR: MR3182634
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Date available: 2013-11-27T09:43:29Z
Last updated: 2015-03-29
Stable URL: http://hdl.handle.net/10338.dmlcz/143519
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