Although interest in spatial regression models has surged in recent years, a comprehensive,
up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial …

This article proposes compactly supported correlation functions, which parameterize the
smoothness of the associated stationary and isotropic random field. The constructions are …

Real estate has historically employed statistical tools designed for independent
observations while simultaneously noting the violation of these assumptions in the form of …

This paper describes how spatial techniques can be used to improve the accuracy of market
value estimates obtained using multiple regression analysis. Rather than eliminating the …

Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of
aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution …

Gaussian processes (GPs) are a widely used model for regression problems in supervised
machine learning. Implementation of GP regression typically requires O (n 3) logic gates. We …

Maximum likelihood estimates of parameters of some spatial models require the
computation of the log-determinant of positive-definite matrices of the formI—αD. whereD is …

We apply a Bayesian hierarchical Poisson spatial interaction model to the paper trail left by
patent citations between high‐technology patents in Europe to identify and measure spatial …

The main purpose of this paper is to use regression models to explore the factors affecting
housing prices as well as apply spatial aggregation to explore the changes of urban space …

Statistical emulators of computer simulators have proven to be useful in a variety of
applications. The widely adopted model for emulator building, using a Gaussian process …