{"id":9809,"date":"2023-09-21T10:15:38","date_gmt":"2023-09-21T10:15:38","guid":{"rendered":"https:\/\/komorebi.ai\/?page_id=9809"},"modified":"2023-09-21T15:45:07","modified_gmt":"2023-09-21T15:45:07","slug":"optimization-approach","status":"publish","type":"page","link":"https:\/\/komorebi.ai\/es\/optimization-approach\/","title":{"rendered":"On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach"},"content":{"rendered":"\t\t
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On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Applied Mathematics and Computation, 2023<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This research addresses the problem of estimating <\/span>smooth curves<\/b> which verify <\/span>structural properties<\/b>, such as sign, monotonicity, and curvature. The paper introduces a new <\/span>mathematical optimization<\/b> formulation for non-negative <\/span>P-splines<\/b>, broadens these results to out-of-range prediction, expands the methods to other constraints and multiple curve fitting, and presents a new open-source Python library, cpsplines.\u00a0<\/span><\/p>

The model has been tested using both simulated and real-life data, including COVID-19 statistics and mortality rates.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A novel method for estimating smooth curves under shape constraints in real-world applications such as pandemic tracking & demographic modelling<\/p><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Where does it apply?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It is applicable in areas like <\/span>pandemic tracking<\/b> and <\/span>demographic analysis<\/b>. The method has been applied to map COVID-19’s progression in Arag\u00f3n, Spain, and to model age-related mortality rates where overlapping of curves is not biologically reasonable.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Why does it matters?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This research introduces a novel method for estimating smooth curves with shape requirements inherent to the nature of the data. The methodology allows the user to incorporate expert knowledge in the fitting procedure, thus\u00a0\u00a0<\/span>yielding <\/span>coherent<\/b> estimates of the response.\u00a0<\/span><\/p>

It supports custom curve fitting to specific needs, providing more insightful results. With its successful use in <\/span>constrained forecasting<\/b>, this methodology presents potential for enhancing future data analysis methods.\u00a0<\/span><\/p>

It is particularly valuable for <\/span>tracking pandemics<\/b> or studying <\/span>demographics<\/b>, aiding effective decision-making for such aims. Moreover, its future potential in multi-dimensional surfaces and variable selections promises to revolutionize its applications further.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Applied Mathematics and Computation, 2023<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\"Manuel<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tManuel Navarro<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tML Operational Researcher<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tHealth, Shape-constrained regression<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Otras publicaciones<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Predicci\u00f3n de Energ\u00eda Fotovoltaica mediante Aprendizaje Autom\u00e1tico utilizando Datos Satelitales<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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F\u00edsica del Pomeron en el LHC<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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El sue\u00f1o autoinformado deficiente se asocia con factores de riesgo de enfermedades cardiovasculares<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Modelos de aprendizaje autom\u00e1tico para mejorar la predicci\u00f3n de la producci\u00f3n de energ\u00eda fotovoltaica en Espa\u00f1a, beneficiando sectores como la energ\u00eda renovable y las finanzas.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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El estudio utiliza la Teor\u00eda de Regge y el intercambio de Pomeron para mejorar la comprensi\u00f3n de las colisiones de hadrones. A largo plazo, influyendo en avances en \u00e1reas como la computaci\u00f3n cu\u00e1ntica, la tecnolog\u00eda m\u00e9dica, la defensa y la energ\u00eda.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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El estudio indica un v\u00ednculo entre el sue\u00f1o deficiente y un mayor riesgo de enfermedades cardiovasculares, enfatizando la necesidad de mejoras en estrategias del sue\u00f1o y pol\u00edticas de salud.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRenewable energy, Predictive Analytics<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach Applied Mathematics and Computation, 2023 Abstract This research addresses the problem of estimating smooth curves which verify structural properties, such as sign, monotonicity, and curvature. The paper introduces a new mathematical optimization formulation for non-negative P-splines, broadens these results to out-of-range prediction, expands […]<\/p>\n","protected":false},"author":1,"featured_media":8766,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"yoast_head":"\nOn constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach - Komorebi AI<\/title>\n<meta name=\"description\" content=\"Novel method estimates smooth curves under shape constraints, useful in applications like pandemic tracking & demographic modelling.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/komorebi.ai\/es\/optimization-approach\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach - Komorebi AI\" \/>\n<meta property=\"og:description\" content=\"Novel method estimates smooth curves under shape constraints, useful in applications like pandemic tracking & demographic modelling.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/komorebi.ai\/es\/optimization-approach\/\" \/>\n<meta property=\"og:site_name\" content=\"Komorebi AI\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-21T15:45:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/komorebi.ai\/wp-content\/uploads\/2023\/07\/healthcare_keygraphic2-2-e1691054366720.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2119\" \/>\n\t<meta property=\"og:image:height\" content=\"1027\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Tiempo de lectura\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/komorebi.ai\/es\/optimization-approach\/\",\"url\":\"https:\/\/komorebi.ai\/es\/optimization-approach\/\",\"name\":\"On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach - 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