Showing papers on "Scatterplot smoothing published in 2009"

Nonparametric Methods for Modeling Nonlinearity in Regression Analysis

Abstract: The linear model and related generalized linear model (GLM) are important tools for sociologists. If the relationships between y (or in the case of the GLM, the linear predictor η) and the xs are linear, these methods provide elegant summaries of the data. However, these methods fail to adequately model underlying relationships if they are characterized by complex nonlinear patterns. In such cases, nonparametric regression, which allows the functional form between y and x to be determined by the data themselves, is more suitable. There are many types of nonparametric simple regression. I focus on locally weighted scatterplot smoothing (lowess or loess) and smoothing splines because they are the most widely used. I also describe additive and generalized additive models (GAM), which allow modeling of categorical dependent variables, and I explain how these methods can handle both parametric and nonparametric (i.e., lowess and smoothing splines) effects for many predictors. Finally, I briefly introduce the m...

Nonlinear locally weighted kriging prediction for spatio-temporal environmental processes

Abstract: In the paper, a nonlinear interpolation procedure for the spatial prediction of an environmental process is proposed. The suggested interpolation is based on the locally weighted scatterplot smoothing method of Cleveland in 1979. This approach is applied to a nonlinear spatio-temporal model. In an empirical study, the PM10 concentration in the Berlin—Brandenburg region of Germany is considered. It is shown that the local approach permits a more structured interpolation of the air pollution. Copyright © 2009 John Wiley & Sons, Ltd.