title: [Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].
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.
authors.affiliation: Facultad de Ciencias Médicas, Escuela de Obstetricia y Puericultura, Universidad de Santiago de Chile, Santiago de Chile, Chile. Electronic address: jairo.vanegas.l@usach.cl.
authors.forename: Jairo
authors.initials: J
authors.lastname: Vanegas
authors.affiliation: Instituto de Nutrición y Tecnología de los Alimentos (INTA), Universidad de Chile, Santiago de Chile, Chile.
authors.forename: Fabián
authors.initials: F
authors.lastname: Vásquez
mesh_terms: D046688:Child Mortality / Q000639:trends; D002675:Child, Preschool; D003364:Costa Rica; D005544:Forecasting; D006305:Health Status Indicators; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016014:Linear Models; D015999:Multivariate Analysis; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D013997:Time Factors
keywords: Estadística no paramétrica; Forecasting; Methods; Metodología; Non-parametric statistics; Predicción