The decline in the unemployment rate is an indication of the success of economic development in a country, so it needs to be managed to a stability point. The unemployment rate is influenced by various factors (predictor variables). One of the most widely used models if there are many predictor variables is regression model, which is how to know the pattern of functional relationships between one response variable with one or more predictor variables. In this study, a nonparametric regression model is developed by expanding the smoothing Spline model. Based on the results obtained, the model can capture cases of unemployment rate well and can make good predictions based on the data obtained.
Digital Object Identifier (DOI)
Hidayat, Rahmat; Nyoman Budiantara, I.; W. Otok, Bambang; and Ratnasari, Vita
"An Extended Model of Penalized Spline with The Addition of Kernel Functions in Nonparametric Regression Model,"
Applied Mathematics & Information Sciences: Vol. 13:
3, Article 18.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol13/iss3/18