Abstract
This study aims to estimate the nonparametric truncated spline path functions of linear, quadratic, and cubic orders at one and two knot points and determine the best model on the variables that affect the timely payment of House Ownership Credit (HOC). In addition, this study aims to test the hypothesis to determine the variables that have a significant effect on punctuality in paying House Ownership Credit (HOC). The data used in this study are primary data. The variables used are service quality and lifestyle as exogenous variables, willingness to pay as mediating variables and on time to pay as endogenous variables. Analysis of the data used in this study is a nonparametric path using R software. The results showed that the best model was obtained on a nonparametric truncated spline linear path model with 2 knot points. The model has the smallest GCV value of 25.9059 and R<sup>2</sup> value of 96.96%. In addition, the results of hypothesis testing on function estimation have a significant effect on the relationship between service quality and willingness to pay, the relationship between service quality and on time to pay, the relationship between lifestyle and willingness to pay, and the relationship between lifestyle and on time pay. The novelty of this research is to model and test the hypothesis of nonparametric regression development, namely nonparametric truncated spline paths of linear, quadratic and cubic orders.
Highlights
IntroductionResearchers usually observe a relationship between variables. One method of statistical analysis that can be used is regression analysis, where regression analysis is a method used to observe the relationship between two or more variables and can be used to determine the pattern of the relationship from a model whose form is not yet known [1].There are three regression analysis approaches that can be done, namely nonparametric, semiparametric, and parametric approaches [2]
In a study, researchers usually observe a relationship between variables
In the nonparametric regression approach the form of the regression function is assumed to be unknown and the linearity assumption is not met, the parametric regression approach is assumed to be known and the linearity assumption is met [3], while the semiparametric regression approach is a combination of parametric and nonparametric regression that can be performed if some of the curves are assumed to be known and some are assumed to be unknown [4]
Summary
Researchers usually observe a relationship between variables. One method of statistical analysis that can be used is regression analysis, where regression analysis is a method used to observe the relationship between two or more variables and can be used to determine the pattern of the relationship from a model whose form is not yet known [1].There are three regression analysis approaches that can be done, namely nonparametric, semiparametric, and parametric approaches [2]. Researchers usually observe a relationship between variables. One method of statistical analysis that can be used is regression analysis, where regression analysis is a method used to observe the relationship between two or more variables and can be used to determine the pattern of the relationship from a model whose form is not yet known [1]. There are three regression analysis approaches that can be done, namely nonparametric, semiparametric, and parametric approaches [2]. Many current studies use more than one dependent variable. If there are two dependent variables, regression cannot be performed. According to Sudaryono [6], path analysis is a method that observes the direct and indirect effects of the hypothesized variables.
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