Resumo

VANEGAS, Jairo  e  VASQUEZ, Fabián. Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series. Gac Sanit [online]. 2017, vol.31, n.3, pp.235-237. ISSN 0213-9111.  https://dx.doi.org/10.1016/j.gaceta.2016.10.003.

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.

Palavras-chave : Methods; Non-parametric statistics; Forecasting.

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