Open AccessBook
Statistical inference for spatial processes
Reads0
Chats0
TLDR
In this article, a likelihood analysis for spatial Gaussian processes and edge correction for spatial point processes are presented. But the analysis is limited to binary images and is not suitable for multilayer images.Abstract:
Introduction 1. Likelihood analysis for spatial Gaussian processes 2. Edge correction for spatial point processes 3. Parameter estimation for Gibbsian point processes 4. Modelling spatial images 5. Summarizing binary images.read more
Citations
More filters
Journal ArticleDOI
Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Book
Spatial Econometrics: Methods and Models
TL;DR: In this article, a typology of Spatial Econometric Models is presented, and the maximum likelihood approach to estimate and test Spatial Process Models is proposed, as well as alternative approaches to Inference in Spatial process models.
Journal ArticleDOI
spatstat: An R Package for Analyzing Spatial Point Patterns
Adrian Baddeley,Rolf Turner +1 more
TL;DR: This paper is a general description of spatstat and an introduction for new users.
Posterior predictive assessment of model fitness via realized discrepancies
TL;DR: In this article, the authors consider Bayesian counterparts of the classical tests for good-ness of fit and their use in judging the fit of a single Bayesian model to the observed data.
References
More filters
Journal ArticleDOI
On multimodality of the likelihood in the spatial linear model
Kanti V. Mardia,Alan Watkins +1 more
TL;DR: In this paper, a power covariance with range parameter is proposed for the spatial linear model and a convenient profile likelihood is introduced and studied in view of potential multimodal likelihoods for small samples.