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<title>Latent variable model inversion for intervals. Application to tolerance intervals in class-modelling situations, and specification limits in process control</title>
<creator>Sánchez Pastor, Mª Sagrario</creator>
<creator>Ortiz Fernández, Mª Cruz</creator>
<creator>Ruiz Miguel, Santiago</creator>
<creator>Valencia García, Olga</creator>
<creator>Sarabia Peinador, Luis Antonio</creator>
<subject>PLS</subject>
<subject>LVMI</subject>
<subject>Tolerance intervals</subject>
<subject>Specification limits</subject>
<description>The paper deals with the inversion of intervals when a PLS (Partial Least Squares) model is used. However, instead of discretizing the interval, it is proved that the region resulting from the inversion of a PLS model is a convex set bounded by two parallel hyperplanes, each corresponding to the direct inversion of each endpoint of the given interval.&#xd;
When the domain of the input variables is a convex set, any feasible solution with predictions within the interval set in the response can be obtained as a convex combination of a point on each of the two hyperplanes. In this way, the new solutions preserve the internal structure of the input variables.&#xd;
This methodology can be of interest in several domains where the response under study is defined in terms of an interval of admissible values, such as specifications for a product in an industrial process, or tolerance intervals for computing compliant class-models.&#xd;
The inversion of the corresponding fitted model defines a region in the input space (predictor variables) whose predictions fall within the specified interval. Then, estimating and exploring this region will increase the information about the problem under study.</description>
<date>2025-01-13</date>
<date>2025-01-13</date>
<date>2024-08</date>
<type>info:eu-repo/semantics/article</type>
<identifier>0169-7439</identifier>
<identifier>http://hdl.handle.net/10259/9875</identifier>
<identifier>10.1016/j.chemolab.2024.105166</identifier>
<language>eng</language>
<relation>Chemometrics and Intelligent Laboratory Systems. V. 251, 105166</relation>
<relation>https://doi.org/10.1016/j.chemolab.2024.105166</relation>
<rights>http://creativecommons.org/licenses/by-nc/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Atribución-NoComercial 4.0 Internacional</rights>
<publisher>Elsevier</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>