Designing of Child Growth Chart Based on Multi-Response Local Polynomial Modeling
- 1 Airlangga University, Indonesia
- 2 , Indonesia
Abstract
Problem statements: Anthropometry measures used to measure physical children growth are not onlyweight but also height and head circumference. In this study we develop the estimation of multi-response localpolynomial regression and apply it to design growth chart for children up to five years old based on three responsevariables i.e., weight, height and head circumference. Approach: Based on local polynomial estimator, wedescribe the estimation of multi-response nonparametric regression model by using weighted least squared. Themodel is applied to design health card of children up to five years old by using children data in Surabaya, Indonesia. Generalized Cross Validation (GCV) method is used to determine the order of local polynomial fit and the bandwidthfor each response variable. Results: We formulate the multi-response local polynomial modeling and give a design of health card of children up to five years old in Surabayacity, Indonesia. Conclusion: The child growth chart based on multi-response local polynomial modeling showsincreasing of children nutrition in Surabaya 2010.Because of the strong correlations among all three response variables,the simultaneosly approach for model estimationis better than partly single response approach. The result of simultaneosly model estimation based on multi-response local polynomial modeling satisfies goodness of fit criterion i.e., mean squared error value tend to zero and determination coefficient value tend to one.
DOI: https://doi.org/10.3844/jmssp.2012.342.347
Copyright: © 2012 Nur Chamidah, I. Nyoman Budiantara, Sony Sunaryo and Ismaini Zain. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Local polynomial
- multi-response
- Generalized Cross Validation (GCV)
- growth chart