Analyzing spatial point patterns subject to measurement error

Analyzing Paradata to Investigate Measurement Error

Analyzing Spatial Point Patterns Using the New SPP Procedure

In many spatial analysis applications (including crime analysis, epidemiology, ecology, and forestry), spatial point process modeling can help you study the interaction between different events and help you model the process intensity (the rate of event occurrence per unit area). For example, crime analysts might want to estimate where crimes are likely to occur in a city and whether they are a...

spatstat: An R Package for Analyzing Spatial Point Patterns

spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and ‘marks’ attached to the points of the point pattern. A unique feature of spatstat is its gen...

Spatial Point Patterns

In looking at ecological processes, interest in the pattern of occurrences of species, e.g., the pattern of trees in a forest, say junipers and pinions. In spatial epidemiology, we seek to find pattern in disease cases, perhaps different patterns for cases vs. controls. Exple: breast cancer cases; treatment option mastectomy or radiation In syndromic surveillance we seek to identify disease out...

Fitting a Finite Mixture Distribution to a Variable Subject to Heteroscedastic Measurement Error

We consider the case where a latent variable X cannot be observed directly and instead a variable W X U with an heteroscedastic measurement error U is observed It is assumed that the distribution of the true variable X is a mixture of normals and a type of the EM algorithm is applied to nd approxi mate ML estimates of the distribution parameters of X