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 2 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.
Violation of the Poisson regression assumption can cause the model formed will produce an unbiased estimator. There is a good method for estimating parameters on small sample sizes and on all distributions, namely the Bayesian method. The number of death from chronic Filariasis data violates the Poisson regression assumption, so it is modeled with the Bayesian Hurdle Poisson Regression. With the Bayesian method, convergence is fullfilled when 300000 iterations and 7 thin are performed. The results showed that in the logit model only one predictor variable had a significant effect on the number of cases of death due to chronic Filiariasis in 34 Provinces in Indonesia . The Truncated Poisson model resulted in all predictor variables having a significant effect on the number of cases of death due to chronic Filariasis.
Poisson regression is one of the model to explain the functional relationship between response variable in the form of count and predictor variable. An important assumption in Poisson Regression analysis is equidispersion. In certain cases, where response variable consists of too many zeros, causing the variance to be greater than the mean or called overdispersion that can be overcomed by the Hurdle model. Filariasis disease is caused by filaria worm that led to swelling of the limbs in humans. One province in Indonesia, Papua Barat, reported a quite high death of chronic filariasis cases with a death rate of 459 people. The Hurdle regression model is appropriately to model the number of cases of chronic filariasis death in Indonesia since the data contains overdispersion. This study will compared two regression Hurdle models, namely the Hurdle Poisson regression and Hurdle Negative Binomial Regression. The results showed that the Negative Binomial Hurdle regression model was better than that of the Hurdle Poisson regression model in modeling cases of filariasis in Indonesia with AIC value of 213.263. Based on logit model of Negative Binomial Regression, the percentage of households that have access to proper sanitation (𝑋 5 ) has a significant effect on the number of cases of death from chronic filariasis in Indonesia.
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