Abstract
In a linear regression framework, structural change models are proposed for the detection of abrupt changes in parameter values. Two models are discussed: 1) the pure structural change model, where all the components of the parameter \(\beta \in \mathbb{R}^{{p,1}} \) are allowed to change and are tested all together, and 2) the partial structural change model, where only some of the parameter β components might change. For an on-line implementation, a sliding window algorithm is introduced. The procedure was successfully applied to phase transition identification in cryogenic thermometry.
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*Work partially funded under EU SofTools_MetroNet Contact N. G6RT-CT-2001-05061.
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Ichim, D., Peroni, I. & Sparasci, F. Structural Change Models with an Application in Cryogenic Thermometry. J Math Model Algor 4, 253–264 (2005). https://doi.org/10.1007/s10852-005-9002-5
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DOI: https://doi.org/10.1007/s10852-005-9002-5