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<dc:title>Latent variable model inversion for intervals. Application to tolerance intervals in class-modelling situations, and specification limits in process control</dc:title>
<dc:creator>Sánchez Pastor, Mª Sagrario</dc:creator>
<dc:creator>Ortiz Fernández, Mª Cruz</dc:creator>
<dc:creator>Ruiz Miguel, Santiago</dc:creator>
<dc:creator>Valencia García, Olga</dc:creator>
<dc:creator>Sarabia Peinador, Luis Antonio</dc:creator>
<dc:subject>PLS</dc:subject>
<dc:subject>LVMI</dc:subject>
<dc:subject>Tolerance intervals</dc:subject>
<dc:subject>Specification limits</dc:subject>
<dc:subject>Investigación operativa</dc:subject>
<dc:subject>Estadística matemática</dc:subject>
<dc:subject>Operations research</dc:subject>
<dc:subject>Mathematical statistics</dc:subject>
<dc: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.</dc:description>
<dc:date>2025-01-13T08:00:33Z</dc:date>
<dc:date>2025-01-13T08:00:33Z</dc:date>
<dc:date>2024-08</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:identifier>0169-7439</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/9875</dc:identifier>
<dc:identifier>10.1016/j.chemolab.2024.105166</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>Chemometrics and Intelligent Laboratory Systems. V. 251, 105166</dc:relation>
<dc:relation>https://doi.org/10.1016/j.chemolab.2024.105166</dc:relation>
<dc:rights>Atribución-NoComercial 4.0 Internacional</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:format>application/pdf</dc:format>
<dc:publisher>Elsevier</dc:publisher>
<europeana:object>https://riubu.ubu.es/bitstream/10259/9875/4/Sanchez-cils_2024.pdf.jpg</europeana:object>
<europeana:provider>Hispana</europeana:provider>
<europeana:type>TEXT</europeana:type>
<europeana:rights>http://creativecommons.org/licenses/by-nc/4.0/</europeana:rights>
<europeana:dataProvider>RIUBU. Repositorio Institucional de la Universidad de Burgos</europeana:dataProvider>
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