An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in line...
An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations
About this item
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s)
t
-distribution. This error model a...
Alternative Titles
Full title
An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations
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Record Identifier
TN_cdi_proquest_journals_2010930026
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2010930026
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ISSN
0949-7714
E-ISSN
1432-1394
DOI
10.1007/s00190-017-1062-6
