Work

Semiparametric Biresponse Regression Modeling Mixed Spline Truncated, Fourier Series, and Kernel in Predicting Rainfall and Sunshine

Year: 2025

Type: article

Abstract: The biresponse semiparametric regression analysis combines parametric and nonparametric components to understand the relationship between two correlated response variables and predictor variables. In this approach, the nonparametric component can be estimated using spline truncated, Fourier series, or kernel methods, each suitable for specific data patterns. This study aims to estimate the parameters of a mixed semiparametric regression model on climate data using the Weighted Least Square (WLS) method and to select optimal knot points, oscillation parameters, and bandwidth based on the smallest Generalized Cross Validation (GCV) value. The results show that the best model combines a spline truncated component with one knot and a Fourier series component with one oscillation, yielding a minimum GCV of 7.401, an R² of 84.66%, and an MSE of 92.33. The findings suggest that the biresponse semiparametric regression model combining spline truncated, Fourier series, and kernel estimators is highly effective for modeling climate data with complex predictor patterns.

Source: Statistics Optimization & Information Computing

DOI: 10.19139/soic-2310-5070-2166

Authors: Hartina Husain, Putri Indi Rahayu, Muhammad Rifki Nisardi, Muhammad Aslam Al-Fadhilah, Ahmad Husain

Institutions: Institut Teknologi Bacharuddin Jusuf Habibie, Universitas Sulawesi Barat

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, Direktorat Jenderal Pendidikan Tinggi

OpenAlex ID: W4412020131

FWCI: 1.736

Cites:

Cited by: 2

Related to: 10