Data Mining Application of Non-Linear Mixed Modeling in Water Quality Analysis

Overview

In regression analysis, non-linearity in fixed and random effects can adversely affect efficiency of regression parameter estimates. Successful non-linear time series modeling would improve regression parameter estimates and produce a richer notion of water quality than linear time series models allow. In addition multiple independent variables make each point in space a finite dimensional vector, non-linear in two dimensions jointly. The SAS/STAT procedure, NLMIXED fits non-linearity successfully in any time series using maximum likelihood-based methods.