Nearest-neighborhood linear regression in an application with software effort estimation

This paper discusses nearest-neighborhood linear regression methods in a statistical view of learning and present an application of these models to software project effort estimation. The usefulness of the models is highlighted through experiments with a well-known NASA software project data set. A comparative study with global regression methods such as bagging predictors, support vector regression, radial basis functions neural networks is also introduced.