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 …
An accurate estimate of resource use by an animal can be summarized by regressing local- and landscape level resources on an individual animal’s or population’s utilization distribution (UD) in a spatially explicit way. The resulting equation is …
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 …
The assessment of environmental risk and the evaluation of environmental policies increasingly require accurate and relevant information about the environment. For policy makers and stakeholders to evaluate possible policy changes, an understanding …
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 …
The traditional best linear unbiased prediction procedure ('Kriging') is used in this paper for inference, but within a Bayesian framework. See Brown, Le and Zidek (1994) for an alternative Bayesian formulation. Our approach is to exam how posterior …
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. …