A multiresponse multipredictor semiparametric regression (MMSR) model is a combination of parametric and nonparametric regressions models with more than one predictor and response variables where there is correlation between responses. Due to this correlation we need to construct a symmetric weight matrix. This is one of the things that distinguishes it from the classical method, which uses a parametric regression approach. In this study, we theoretically developed a method of determining a confidence interval for parameters in a MMSR model based on a truncated spline, and investigating asymptotic properties of estimator for parameters in a MMSR model, especially consistency and asymptotic normality. The weighted least squares method was used to estimate the MMSR model. Next, we applied a pivotal quantity method, a Cramer–Wold theorem, and a Slutsky theorem to determine the confidence interval, investigate consistency, and asymptotic normality properties of estimator for parameters in a MMSR model. The obtained results were that the estimated regression function is linear to observation. We also obtained a $100\left(1-\alpha \right)\%$ confidence interval for parameters in the MMSR model, and the estimator for parameters in MMSR model was consistent and asymptotically normally distributed. In the future, these obtained results can be used as a theoretical basis in designing a standard toddlers growth chart to assess nutritional status.

When handling longitudinal data in regression models, we often encounter problems involving two interrelated response variables. These response variables may display an unknown curve shape in their relationship with one predictor variable, referred to as the nonparametric component, while maintaining a linear relationship with other predictor variables, referred to as the parametric component. In such cases, a Biresponse Semiparametric Regression (BSR) approach is a suitable solution. This research aims to estimate the BSR model for longitudinal data using the Local Polynomial Kernel (LPK) estimator by considering a symmetrical variance–covariance matrix estimate validated on simulation data and apply it to a real dataset of Dengue Hemorrhagic Fever (DHF) disease. The parameter estimation method used is a combination of Least Squares (LS) and Weighted Least Squares (WLS). For determining the optimal bandwidth, we use a Generalized Cross–Validation (GCV) method. The simulation study results indicate that with kernel weighting, employing weights derived from the inverse of the variance–covariance matrix significantly enhances the estimation accuracy of the BSR model. In addition, the results of the estimation for modeling the DHF disease, where platelets and hematocrit are response variables, and hemoglobin and examination time are predictor variables, produced an R-Square value of 92.8%.

Jumlah penumpang kereta api di Indonesia kembali mengalami peningkatan semenjak masa pandemi. Salah satu stasiun yang mengalami peningkatan penumpang adalah Stasiun Surabaya Gubeng. Penelitian ini bertujuan untuk mendapatkan hasil prediksi jumlah penumpang harian kereta api di Stasiun Surabaya Gubeng menggunakan metode Monte Carlo dengan pembangkit bilangan acak yang berbeda. Metode Monte Carlo merupakan metode yang menginterpretasikan hasil ketidakpastian probabilitas dari suatu proses dan menyimulasikan nilai frekuensi secara stokastik dari segala kemungkinan hasil. Pembangkit bilangan acak yang digunakan yaitu; multiplicative, mixed, dan random uniform. Tingkat keakuratan dari hasil penelitian dihitung berdasarkan nilai Mean Absolute Percentage Error (MAPE). Data dalam penelitian ini merupakan data time series diambil dari tanggal 16 Mei 2022 hingga 2 Oktober 2022 sebanyak 140 hari. Data dibagi menjadi tujuh kelompok berdasarkan nama hari sebanyak 20 data untuk setiap kelompok. Prediksi dilakukan menggunakan Monte Carlo diperoleh rata-rata nilai MAPE outsample dari setiap kelompok hari yaitu; hari Senin sebesar 25,25%, hari Selasa sebesar 16,74%, hari Rabu sebesar 17,73%, hari Kamis sebesar 3,32%, hari Jumat sebesar 12,36%, hari Sabtu sebesar 4,88%, dan hari Minggu sebesar 2,62%. Kesimpulan akhir diperoleh bahwa hasil prediksi sangat akurat terjadi pada hari Kamis, Sabtu dan Minggu.

In Jepara, Central Java, Indonesia, significant correlations between high rainfall and wind speed impact multiple sectors including health, agriculture, and infrastructure. This study aims to predict the effects of extreme weather by employing nonparametric regression based on Fourier series estimators. Data from December 2023 to March 2024, sourced from NASA, were analyzed using sinus, cosinus, and combined Fourier functions to model the dynamic and seasonal fluctuations of weather variables. This approach allows for a flexible modeling of these previously undefined functional relationships. The analysis revealed that the combined function model was superior, achieving an optimal Generalized Cross-Validation (GCV) score of 0,236498 with a Fourier coefficient K=3, indicating a well-fitted model. Moreover, this model exhibited a low Mean Absolute Percentage Error (MAPE) of 1,887, demonstrating high predictive accuracy. These findings not only affirm the efficacy of Fourier series in nonparametric regression for weather forecasting but also underscore its potential in informing public policy and bolstering disaster preparedness in Jepara and similar regions vulnerable to extreme weather conditions

Regression analysis is one of the statistical methods used to model the relationship between response variables and predictor variables. Semiparametric regression is a combination of parametric and nonparametric regression. The estimator used in estimating the semiparametric regression model in this research is the Local Polynomial. Longitudinal data can be found in the health sector, including dengue hemorrhagic fever (DHF) data. The laboratory criteria for indication of DHF is thrombocytopenia. This research aims to obtain platelets model for DHF patients that can be used for forecasting so that it is hoped that it can provide information to the medical team in treating DHF patients. The estimated model used is Local Polynomial semiparametric regression on longitudinal data. The response variables in this research were platelets of DHF patients, which were influenced by hemoglobin as parametric predictor variable and examination time while hospitalized as nonparametric predictor variable. In the local polynomial regression model, it is necessary to select the optimal bandwidth and polynomial order method, GCV. The optimum bandwidth selection based on the GCV method obtained is 1.5 and polynomial order of 2, then applied to DHF patient platelet data, producing an estimated local polynomial semiparametric regression model that follows the actual data pattern. Modeling the platelets of DHF patients obtained using a local polynomial estimator resulted in an R2 value of 84.25% and MAPE of 4.5%, indicating highly accurate forecasting, so it can be concluded that the resulting model is better at predicting.

Support for one of the points of Sustainable Development Goals (SDGs), namely Zero Hunger, is by supporting sustainable agricultural empowerment. Indonesia is one of the countries with the fourth largest rice consumption according to the United States Department of Agriculture. 90% of Indonesians consume rice as a staple food. In this study, we model paddy production in Indonesia using a semiparametric time series regression approach based on least square spline estimator (LSSE). Where spline is used to overcome data that tends to fluctuate in monthly paddy production data. Monthly data on paddy production in Indonesia over a certain period of time is used to build a model. The use of a semiparametric regression approach by combining parametric components and nonparametric components for analyzing factors that affect paddy production. In this study, the parametric component is paddy production in the previous period lag-1 and the nonparametric components are the potential area of crop failure and the generative area. For predicting paddy production in Indonesia using Semiparametric Time Series Regression Model (STSRM) approach based on LSSE, we determine the order and optimal knot points based on the smallest Generalized Cross Validation (GCV) value. The results of the study show that the Mean Absolute Percentage Error (MAPE) value of 18.05% is less than 20%. It means that prediction of paddy production in Indonesia using STSRM based on LSSE is a good prediction