Space-varying regression models: specifications and simulation

Space-varying regression models: specifications and simulation

Space-varying regression models are generalizations of standard linear models where the regression coe4cients are allowed to change in space. The spatial structure is speci%ed by a multivariate extension of pairwise di6erence priors, thus enabling incorporation of neighboring structures and easy sampling schemes. Bayesian inference is performed by incorporation of a prior distribution for the h...

Dynamic spatial regression models for space-varying forest stand tables

Inference of Time - Varying Regression Models

We consider parameter estimation, hypothesis testing and variable selection for partially time-varying coefficient models. Our asymptotic theory has the useful feature that it can allow dependent, nonstationary error and covariate processes. With a two-stage method, the parametric component can be estimated with a n-convergence rate. A simulation-assisted hypothesis testing procedure is propose...

Varying-Coefficient Functional Linear Regression Models

We propose in this work a generalization of the functional linear model in which an additional real variable influences smoothly the functional coefficient. This leads us to build a varying-coefficient regressionmodel for functional data. We propose two estimators based respectively on conditional functional principal regression and on local penalized regression splines and prove their pointwis...

Nonlinear Regression Analysis and Nonlinear Simulation Models

This paper is a survey of SAS System features for nonlinear models, with emphasis on new features for nonlinear regression. Topics include automatic calculation of analytic derivatives, estimation with nonlinear parameter restrictions, tests of nonlinear hypotheses, maximum likelihood and generalized method of moments (GMM) estimation, estimation of simultaneous systems of nonlinear regression ...