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Multivariate Adaptative Regression Splines (MARS), una alternativa para el análisis de series de tiempo / Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series
Vanegas, Jairo; Vásquez, Fabián.
Affiliation
  • Vanegas, Jairo; Universidad de Santiago de Chile. Facultad de Ciencias Médicas, Escuela de Obstetricia y Puericultura. Santiago de Chile. Chile
  • Vásquez, Fabián; Universidad de Chile. Instituto de Nutrición y Tecnología de los Alimentos (INTA). Santiago de Chile. Chile
Gac. sanit. (Barc., Ed. impr.) ; 31(3): 235-237, mayo-jun. 2017. tab, ilus, graf
Article in Spanish | IBECS | ID: ibc-162088
Responsible library: ES1.1
Localization: BNCS
RESUMEN
Multivariate Adaptative Regression Splines (MARS) es un método de modelación no paramétrico que extiende el modelo lineal incorporando no linealidades e interacciones de variables. Es una herramienta flexible que automatiza la construcción de modelos de predicción, seleccionando variables relevantes, transformando las variables predictoras, tratando valores perdidos y previniendo sobreajustes mediante un autotest. También permite predecir tomando en cuenta factores estructurales que pudieran tener influencia sobre la variable respuesta, generando modelos hipotéticos. El resultado final serviría para identificar puntos de corte relevantes en series de datos. En el área de la salud es poco utilizado, por lo que se propone como una herramienta más para la evaluación de indicadores relevantes en salud pública. Para efectos demostrativos se utilizaron series de datos de mortalidad de menores de 5 años de Costa Rica en el periodo 1978-2008 (AU)
ABSTRACT
Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008 (AU)
Subject(s)

Full text: Available Collection: National databases / Spain Health context: SDG3 - Health and Well-Being Health problem: Target 3.2: Reduce avoidable death in newborns and children under 5 Database: IBECS Main subject: Time Series Studies / Multivariate Analysis / Regression Analysis Type of study: Diagnostic study / Prognostic study / Risk factors Language: Spanish Journal: Gac. sanit. (Barc., Ed. impr.) Year: 2017 Document type: Article Institution/Affiliation country: Universidad de Chile/Chile / Universidad de Santiago de Chile/Chile

Full text: Available Collection: National databases / Spain Health context: SDG3 - Health and Well-Being Health problem: Target 3.2: Reduce avoidable death in newborns and children under 5 Database: IBECS Main subject: Time Series Studies / Multivariate Analysis / Regression Analysis Type of study: Diagnostic study / Prognostic study / Risk factors Language: Spanish Journal: Gac. sanit. (Barc., Ed. impr.) Year: 2017 Document type: Article Institution/Affiliation country: Universidad de Chile/Chile / Universidad de Santiago de Chile/Chile
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