Considering the field of functional data analysis, we developed a new Bayesian method for variable selection in function-on-scalar regression (FOSR). Our approach uses latent variables, allowing an adaptive selection since it can determine the number of variables and which ones should be selected for a function-on-scalar regression model. Simulation studies show the proposed method's main properties, such as its accuracy in estimating the coefficients and high capacity to select variables correctly. Furthermore, we conducted comparative studies with the main competing methods, such as the BGLSS method as well as the group LASSO, the group MCP and the group SCAD. We also used a COVID-19 dataset and some socioeconomic data from Brazil for real data application. In short, the proposed Bayesian variable selection model is extremely competitive, showing significant predictive and selective quality.

翻译：考虑到功能性数据分析领域,我们开发了一种新的巴伊西亚方法,用于在功能在天际回归中进行变量选择。我们的方法使用潜在变量,允许进行适应性选择,因为它可以确定变量的数量和哪些变量应该选择到一个功能在天际回归模型中。模拟研究表明了拟议方法的主要特性,如其在估计系数方面的准确性和正确选择变量的高度能力。此外,我们与主要竞争方法进行了比较研究,如BGLSS方法以及LASSO、MCP集团和SCAD集团。我们还使用COVID-19数据集和巴西的一些社会经济数据用于实际数据应用。简而言之,拟议的巴伊西亚变量选择模型具有极大的竞争力,显示了显著的预测性和选择性质量。</s>