Reformulating land-use regression method as sign-constrained regularized regressions: Advantages and improvements - Kwon, Soon Sun; Choi, Hosik; Lee, Whanhee; Kim, Yeonjin; Kim, Hwan Cheol; Lee, Woojoo (Environmental Modelling and Software, 2023)
Land-use regression is a popular method for predicting ambient pollutant concentrations at points of interest where no measurements are taken. However, the model-building process is complicated, and systematically understanding when and how the process works is difficult. To overcome these limitations, we reformulate the existing land use regression method as a sign-constrained regression problem with an explicit objective function to be minimized. This novel formulation always leads to estimated regression coefficients that satisfy the predefined direction based on subject matter knowledge while simultaneously substantially improving the prediction performance of the existing land-use regression method. The advantages of the proposed sign-constrained regression method are confirmed through a numerical study and real data analysis.
Keywords: Ambients; Building process; Constrained regression; Interpretability; Land use regression; Penalized regression method; Pollutant concentration; Regression method; Regression problem; Sign constraints; Interpretability; Land use regression; Penalized regression methods; Prediction; Sign constraints