Likelihood: Journal of Statistics and Its Application

Bayesian Fitting of Univariate Skew Normal Distribution on Padua Temperature Dataset

2025-11-25T08:30:47+00:00

Data-driven distributional exploration is an important issue in selecting appropriate statistical methods. Due to the presence of skewness that deviates from the normal distribution, the annual average temperature data of Padua cannot be adequately fitted using a symmetric distribution such as the normal distribution. To address this issue, this study aims to fit a skew normal distribution on Padua temperature dataset. Under a Bayesian approach, we specified priors and constructed posterior distributions as the basis for inference. The sampling results and diagnostics indicate that the chains satisfy the convergence criteria. The posterior means for the three skew normal parameters are 12.05, 1.04, and 2.26, corresponding to the location, scale, and shape parameters, respectively. Lastly, this study not only relaxes the assumption of normality but also provides a modeling foundation for Padua temperature data that can support prediction, forecasting, mapping, and other advanced data analysis purposes.

Estimation of Geographically Weighted Regression With Fixed Kernel Gaussian

2026-03-30T08:21:35+00:00

In spatial analysis, the relationship between predictor and response variables is often not uniform across regions, a condition known as spatial heterogeneity. Global regression models such as Ordinary Least Squares (OLS) assume that regression parameters are constant across all locations, meaning that the effects of independent variables are considered identical for every observational unit. This assumption is often unrealistic because it does not account for differences in regional characteristics. If spatial heterogeneity is ignored, model estimates may become less accurate and fail to explain local variations adequately. Geographically Weighted Regression (GWR) is a model that allows regression parameters to vary according to geographic location. Under this approach, each region obtains its own local parameter estimates, enabling the model to capture spatially varying relationships more flexibly. The results indicate that the AIC value of the GWR model with Fixed Gaussian Kernel (347.8637) is smaller than that of the OLS model (382.1161), suggesting that GWR provides a better fit. Empirically, the Human Development Index (HDI) in 53 districts/cities is influenced by Life Expectancy, Expected Years of Schooling, and Per Capita Expenditure, while in 66 districts/cities it is influenced by Life Expectancy, Expected Years of Schooling, Poverty Rate, and Per Capita Expenditure.

Concrete Compressive Strength Prediction System Using K-Nearest Neighbor (KNN) Regression with k-Parameter Tuning Implementation

2026-06-17T14:21:06+00:00

Concrete compressive strength is one of the primary parameters determining the quality and suitability of concrete in construction. Conventional compressive strength testing requires considerable time, cost, and specialized laboratory equipment; therefore, a more efficient and accurate alternative approach is needed. This study aims to develop a concrete compressive strength prediction system using the K-Nearest Neighbor (KNN) Regression method with k-parameter tuning. The dataset used is the Concrete Compressive Strength Dataset from the UCI Machine Learning Repository, consisting of 1,030 samples with eight input variables and one target variable. The research stages include feature engineering, data preprocessing, train-test data splitting, KNN model development, and optimization of the k parameter using the 10-fold Repeated Cross-Validation method. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results show that the optimal k value is k = 3, yielding the best predictive performance with an R² of 0.88 on the test data, an RMSE of 5.56 MPa, and an MAE of 3.74 MPa. The engineered features LogAge and Water-Cement Ratio are identified as the most influential factors affecting concrete compressive strength. Overall, the findings demonstrate that KNN Regression with k-parameter tuning can provide accurate and stable predictions of concrete compressive strength. This model is expected to serve as a practical alternative for supporting concrete quality control and decision-making processes in construction more quickly and efficiently.