Abstract
In this contribution we focus on dwell times a user spends on various areas of a web site within a session. We assume that dwell times may be adequately modeled by a Weibull distribution which is a flexible and common approach in survival analysis. Furthermore we introduce heterogeneity by various parameterizations of dwell time densities by means of proportional hazards models. According to these assumptions the observed data stem from a mixture of Weibull densities. Estimation is based on EM-algorithm and model selection may be guided by BIC. Identification of mixture components corresponds to a segmentation of users/sessions. A real life data set stemming from the analysis of a world wide operating eCommerce application is provided. The corresponding computations are performed with the mixPHM package in R.
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Data Analysis, Machine Learning and Applications |
| Herausgeber*innen | C. Preisach, H. Burghardt, L. Schmidt-Thieme & R. Decker |
| Erscheinungsort | Berlin |
| Verlag | Springer |
| Seiten | 593 - 600 |
| Publikationsstatus | Veröffentlicht - 1 März 2008 |