Bayesian Nonparametric Truncated Spline Regression for Modeling Nutritional and Physical Stunting

Septi Nafisa Ulluya Zahra, Adji Ahmad Rinaldo Fernandes, Achmad Efendi, Alfiyah Hanun Nasywa, Fachira Haneinanda Junianto

Abstract


Stunting is a problem that is affected by the socioeconomic and environmental conditions of the public. The present study evaluates the impact of the financial state, environmental quality, and child feeding practices on the nutritional and physical stunting using a Bayesian nonparametric truncated spline regression model. To do this, a single knot spline structure was used a capture non-linear affects and thresholds, posterior estimation being conductied with Gibb’s sampling. The results exhibit that all of the three predictors have a significance after the knot point on the right arrives, indiacting to saturation affects. As for the economic standing and the environmental quality, their effect is consistent, while feeding practices hold a more considerable impact on the nutritional stunting. From model diagnostics, the model had a good fit and predictive accuracy. The results highlight the importance of feeding practices and economic improvement and environmental sanitation, and display the benefits of the Bayesian spline technique for handling complex data.

Keywords


Biresponse Regression; Spline Regression; Truncated Spline; Bayesian Regression; Stunting

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DOI: http://dx.doi.org/10.30829/zero.v9i2.26759

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