A particle swarm optimization approach for estimating parameter confidence regions

Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires determination of parameter space confidence regions in addition to point estimates. In most practical applications, the relation of the parameters to model fitness is highly nonlinear and noisy data leads to further deviations. Thus the confidence regions obtained by using locally linearized models are often misleading. Uniform covering by probabilistic rejection (UCPR) is a robust technique that has been developed to solve this problem, and has been proven to be more efficient than other approximate random search techniques. In this paper, we propose a contour particle swarm optimization (C-PSO) technique and compare its performance against UCPR in predicting the confidence regions. Results indicate that for problems with low number of parameters, both the algorithms are quite comparable. However, real world m...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Praveen Koduru, Stephen Welch, Sanjoy Das
• Keywords: Confidence Regions, GECCO 2007, Parameters, Particle Swarm Optimization