Search Results (6)

Rural poverty and unemployment remain persistent challenges in Indonesia, particularly in regions where agricultural development is uneven and land conversion accelerates socio-economic disparities. These conditions are highly relevant because rural areas serve as the backbone of food security, labour supply, and national economic stability. This study aims to address these issues by developing a flexible analytical framework that simultaneously models three indicators of rural development—rural poverty, rural unemployment, and agricultural sector growth—using a truncated spline nonparametric regression approach with longitudinal data from 2015 to 2023. The methodological approach integrates this regression with panel data across five Indonesian regions, allowing the analysis to capture nonlinear relationships and regional variations that conventional parametric models may overlook. The results indicate that population migration, land use change, and village fund allocation are the dominant drivers of rural development indicators, with nonlinear and region-specific effects. Village funds consistently reduce poverty and unemployment, while excessive land conversion restricts agricultural sector growth. The findings contribute to theory by demonstrating the advantages of flexible nonparametric approaches in modelling rural development dynamics, and to practice by offering empirical evidence for more targeted and adaptive policy interventions to alleviate poverty, reduce unemployment, and strengthen rural resilience. Full article

In Indonesia, one of the main causes of death for both young and elderly people is heart attacks, and the main cause of heart attacks is non-communicable diseases such as hypertension. Deaths due to heart attacks caused by non-communicable diseases, namely hypertension, rank first in Indonesia. Therefore, predictions of the risk of having a heart attack caused by hypertension need serious attention. Further, for determining whether a patient is experiencing a heart attack, an effective method of prediction is required. One efficient approach is to use statistical models. This study discusses predicting risk of heart attack via modeling and classifying hypertension risk based on factors that influence it, namely, age, cholesterol levels, and triglyceride levels by using the spline estimator of the Nonparametric Ordinal Logistic Regression (NOLR) model. In this study, we assume an ordinal scale response variable with q categories to have an asymmetric distribution, namely, a multinomial distribution. The data used in this study are secondary data from medical records of cardiac poly patients at the Haji General Hospital in Surabaya, Indonesia. The results show that the proposed model approach has the greatest classification accuracy and sensitivity values compared to NOLR model approach using GAM, and the classical model approach, namely the Parametric Ordinal Logistic Regression (POLR) model. This means that the NOLR model approach is suitable for predicting hypertension and heart attack risks. Also, the NOLR model estimated using the LS-Spline estimator obtained is valid for predicting the risk of heart attack with accuracy value of 85% and sensitivity value of 100%. Full article

This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT), Indonesia. The study was conducted in the Central Sumba District of ENT province. A classification and regression tree (CART) for land cover classification was analyzed using machine learning techniques through the implementation of the Google Earth Engine. A Focus Group Discussion (FGD) survey followed by in-depth interviews was conducted for primary data collection, involving a total of 871 respondents. The socio-economic data were statistically analyzed descriptively using non-parametric tests. The study showed that (1) there has been a substantial change in land use during the devolution era that has both positive and negative implications for food security and environmental services; (2) there has been population pressure in fertile and agricultural land as a direct impact of the development of city infrastructure; and (3) national intervention through the Food Estate program has fostered and shaped land use change and land management in the Central Sumba District. The study highlights the importance of the devolution spirit in aiding the management of limited arable/agricultural land in predominantly semi-arid areas to ensure food security and environmental services. Full article

Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called the quantile regression model, is particularly useful when dealing with outliers or non-standard conditional distributions in the data. However, when the functions of the covariates are not easily specified in a parametric manner, a nonparametric regression technique is often employed. One such technique is the use of B-splines, a nonparametric approach used to estimate the parameters of the unspecified functions in the model. B-splines smoothing has potential to overfit when the number of knots is increased, and thus, a penalty is added to the quantile objective function known as P-splines. The estimation procedure involves minimizing the quantile loss function using an LP-Problem technique. This method was applied to upper respiratory tract infection data in the city of Bandung, Indonesia, which were measured monthly across 30 districts. The results of the study indicate that there are differences in the effect of covariates between quantile levels for both space and time coefficients. The quantile curve estimates also demonstrate robustness with respect to outliers. However, the simultaneous estimation of the quantile curves produced estimates that were relatively close to one another, meaning that some quantile curves did not depict the actual data pattern as precisely. This suggests that each district in Bandung City not only has different categories of incidence rates but also has a heterogeneous incidence rate based on three quantile levels, due to the difference in the effects of covariates over time and space. Full article

Longitudinal data modeling is widely carried out using parametric methods. However, when the parametric model is misspecified, the obtained estimator might be severely biased and lead to erroneous conclusions. In this study, we propose a new estimation method for longitudinal data modeling using a mixed estimator in nonparametric regression. The objective of this study was to estimate the nonparametric regression curve for longitudinal data using two combined estimators: truncated spline and local linear. The weighted least square method with a two-stage estimation procedure was used to obtain the regression curve estimation of the proposed model. To account for within-subject correlations in the longitudinal data, a symmetric weight matrix was given in the regression curve estimation. The best model was determined by minimizing the generalized cross-validation value. Furthermore, an application to a longitudinal dataset of the poverty gap index in Bengkulu Province, Indonesia, was conducted to illustrate the performance of the proposed mixed estimator. Compared to the single estimator, the truncated spline and local linear mixed estimator had better performance in longitudinal data modeling based on the GCV value. Additionally, the empirical results of the best model indicated that the proposed model could explain the data variation exceptionally well. Full article

Nonparametric regression becomes a potential solution if the parametric regression assumption is too restrictive while the regression curve is assumed to be known. In multivariable nonparametric regression, the pattern of each predictor variable’s relationship with the response variable is not always the same; thus, a combined estimator is recommended. In addition, regression modeling sometimes involves more than one response, i.e., multiresponse situations. Therefore, we propose a new estimation method of performing multiresponse nonparametric regression with a combined estimator. The objective is to estimate the regression curve using combined truncated spline and Fourier series estimators for multiresponse nonparametric regression. The regression curve estimation of the proposed model is obtained via two-stage estimation: (1) penalized weighted least square and (2) weighted least square. Simulation data with sample size variation and different error variance were applied, where the best model satisfied the result through a large sample with small variance. Additionally, the application of the regression curve estimation to a real dataset of human development index indicators in East Java Province, Indonesia, showed that the proposed model had better performance than uncombined estimators. Moreover, an adequate coefficient of determination of the best model indicated that the proposed model successfully explained the data variation. Full article