Diggle, Peter J. and Mateu, J. and Clough, H. E. (2000) A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns. Advances in Applied Probability, 32 (2). pp. 331-343. ISSN 1475-6064Full text not available from this repository.
The paper compares non-parametric (design-based) and parametric (model-based) approaches to the analysis of data in the form of replicated spatial point patterns in two or more experimental groups. Basic questions for data of this kind concern estimating the properties of the underlying spatial point process within each experimental group, and comparing the properties between groups. A non-parametric approach, building on work by Diggle et. al. (1991), summarizes each pattern by an estimate of the reduced second moment measure or K-function (Ripley (1977)) and compares mean K-functions between experimental groups using a bootstrap testing procedure. A parametric approach fits particular classes of parametric model to the data, uses the model parameter estimates as summaries and tests for differences between groups by comparing fits with and without the assumption of common parameter values across groups. The paper discusses how either approach can be implemented in the specific context of a single-factor replicated experiment and uses simulations to show how the parametric approach can be more efficient when the underlying model assumptions hold, but potentially misleading otherwise.
|Journal or Publication Title:||Advances in Applied Probability|
|Uncontrolled Keywords:||Expected significance levels ; K-function ; pseudo-likelihood function ; replicated spatial point patterns ; spatial analysis of variance|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Health and Medicine > Medicine|
Faculty of Science and Technology > Mathematics and Statistics
|Deposited On:||21 Nov 2008 09:41|
|Last Modified:||29 Mar 2017 01:16|
Actions (login required)