Seven of the eleven papers in this collection explain how to estimate discrete dependent variables with spatial dependence using maximum likelihood and how to estimate binary and count
dependent variables using Bayesian methods. A generic algorithm for numerically accurate likelihood evaluates spatial models characterized by a high-dimensional latent Gaussian process and
non-Gaussian response variables. The remaining four papers address continuous dependent variables for modeling group interaction in research, the spillover effects of public capital stock,
government and industry impacts on innovation, and Boston housing data. Distributed in North America by Turpin Distribution. Annotation ©2017 Ringgold, Inc., Portland, OR (protoview.com)
