Estimation of children growth curve based on kernel smoothing in multi-response nonparametric regression

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33 Citations (Scopus)

Abstract

Physical children growth is measured by using anthropometric measures i.e. weight, height and head circumference. The children around two years old grow rapidly, and than decrease slowly along with increasing of children age. It means that locally model approach is more appropriate to the data. Kernel smoothing is one of estimation methods in nonparametric regression. In this paper, we study about Kernel smoothing in multi-response nonparametric regression model and apply it for estimating children up to five years old growth. The model consists of three response variables i.e. weight, height and head circumference, and age as a predictor variable. For determining optimal bandwidth for each response variable, we use cross-validation method. Based on children data in Surabaya 2010, and the 50th percentiles estimation of weight, height and head circumference versus age, we obtain the mean squared error value is 0.05583 and coefficient of determination is 99.99%. The estimation model of children growth curve based on multi-respon kernel smoothing shows fluctuation of the curve and gives mean squared error value tends to zero and coefficient of determination tends to one. These facts mean that the estimation has satisfied goodness of fit criterion.

Original languageEnglish
Pages (from-to)1839-1847
Number of pages9
JournalApplied Mathematical Sciences
Volume7
Issue number37-40
DOIs
Publication statusPublished - 2013

Keywords

  • Children growth curve
  • Kernel smoothing
  • Multi-response
  • Nonparametric regression

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