Regression analysis with spatially-varying coefficients using generalized additive models (GAMs)

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-10-29 DOI:10.1016/j.chemolab.2024.105254

Francisco de Asis López , Javier Roca-Pardiñas , Celestino Ordóñez

引用次数: 0

Abstract

Regression models for spatial data have attracted the attention of researchers from different fields given their widespread application. In this work we analyze the utility of generalized additive models (GAMs) as regression methods with spatially-dependent coefficients. Particularly, three different aspects of the regression analysis were addressed: model definition and estimation, testing spatial heterogeneity, and variable selection. Spatial heterogeneity was addressed through bootstrapping, while and algorithm using the Bayesian Information Criterion (BIC) was implemented for variable selection to reduce computation time. In addition, this study makes a comparison of GAMs with two of the most common methods for regression with spatially-varying coefficients: Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), using both synthetic and real data.

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利用广义加法模型（GAMs）对空间变化系数进行回归分析

空间数据回归模型的广泛应用吸引了不同领域研究人员的关注。在这项工作中，我们分析了广义加法模型（GAMs）作为回归方法的实用性，以及与空间相关的系数。尤其是回归分析的三个不同方面：模型定义和估计、空间异质性测试和变量选择。空间异质性是通过引导法解决的，而变量选择则采用了贝叶斯信息准则（BIC）算法，以减少计算时间。此外，本研究还将 GAM 与两种最常用的空间变化系数回归方法进行了比较：地理加权回归（GWR）和多尺度地理加权回归（MGWR）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.