Tayewo Roméo, Septier François, Nevat Ido, Peters Gareth W
Univ Bretagne Sud, CNRS UMR 6205, LMBA, F-56000 Vannes, France.
TUMCREATE, 1 Create Way, #10-02 CREATE Tower, Singapore 138602, Singapore.
Entropy (Basel). 2023 Aug 29;25(9):1272. doi: 10.3390/e25091272.
We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general regression relationships than classical linear ones through the use of generalized linear models (GLMs) and also captures the inherent structural dependencies or relationships of the data through this regularization based on the graph Laplacian. We use a publicly available dataset from the National Centers for Environmental Information (NCEI) in the United States of America and perform statistical inferences of future CO2 emissions in 59 counties. We empirically show how the proposed method outperforms widely used methods, such as the ordinary least squares (OLS) and ridge regression for this challenging problem.
我们开发了一种用于时空数据的新模型。更具体地说，为了基于广义线性模型估计时空混合效应模型的未知参数，在成本函数中纳入了一个图惩罚函数。通过使用广义线性模型（GLMs），该模型允许比经典线性模型更灵活、更通用的回归关系，并且还通过基于图拉普拉斯算子的这种正则化来捕捉数据固有的结构依赖性或关系。我们使用了来自美国国家环境信息中心（NCEI）的一个公开可用数据集，并对59个县未来的二氧化碳排放量进行统计推断。我们通过实证表明，对于这个具有挑战性的问题，所提出的方法如何优于广泛使用的方法，如普通最小二乘法（OLS）和岭回归。
