Estimation of children growth curve based on kernel smoothing in
multi-response nonparametric regression
Mathematics
Department, Faculty of Sciences and Technology, Airlangga University, Indonesia
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 50thpercentiles 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. © 2013 Nur Chamidah and Toha Saifudin.
Full Text :
Tidak ada komentar:
Posting Komentar