This paper focuses on spatial and temporal modeling of point processes on linear networks. Point processes on linear networks can simply be defined as point events occurring on or near line segment network structures embedded in a certain space. A …
We develop a Bayesian model–based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction …
Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial …
Many analytical techniques that assess resource selection focus on individual relocation points as the sample unit and classify resources as either used or available. Commonly, the relative use of each resource is quantified as the number of …
Statistical analyses were performed on spatial distributions of mushroom green mold foci caused by Trichoderma spp. in 30 standard Pennsylvania doubles (743 $m^2$ production surface) selected at random from over 900 total crops mapped. Mapped …
In this article we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are temperatures at the …
This is the rejoinder to the discussion of 'An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields' (https://doi.org/10.1080/01621459.1994.10476754)
This is a comment of 'Estimating or Choosing a Geostatistical Model' by Oliver Dubrule. His paper was presented at a conference to honour the remarkable contribution of Michel David in the inception, establishment and development of Geostatistics. …
Spatial phenomena is commonly modelled as a realization from a stochastic process. Even when the reality is unique such models can usefully represent the uncertainty the modeler has about the phenomena. This paper is concerned with predicting for …
This article is concerned with predicting for Gaussian random fields in a way that appropriately deals with uncertainty in the covariance function. To this end, we analyze the best linear unbiased prediction procedure within a Bayesian framework. …