Year: 2007
Type: article
Abstract: Robust regression and local polynomial smoothing are applied to the inverse problem for the logistic differential equation (DE) model, in order to develop a more objective, accurate and automatable trends model. A method of inferring the time shift parameter is proposed and applied, allowing the closed form solution of the DE to be used for the prediction of ownership levels in Johannesburg. A simulation study is employed to verify and evaluate the application of non-linear regression to the inverse problem. It is demonstrated that considerable improvements in accuracy, over the transformation to linear-form method, can be obtained. However, on application to actual data, the non-linear regression algorithm fails to converge. The appropriateness of the methods in the case of lower asymptote and early growth phase data, and heterogeneous populations are investigated by simulation. 1. BACKGROUND The car ownership data available to the City is from the vehicle register. Typically, data from administrative sources contain a large proportion of outliers. Moreover, car ownership
Author: J. Daniel Kelly
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W780390764