Statistics Seminar
| Date/Time: | Monday, 21 Oct 2013 from 4:10 pm to 5:00 pm |
|---|---|
| Location: | Snedecor 3105 |
| Cost: | Free |
| URL: | www.stat.iastate.edu |
| Contact: | Jeanette La Grange |
| Phone: | 515-294-3440 |
| Channel: | College of Liberal Arts and Sciences |
| Categories: | Lectures |
| Actions: | Download iCal/vCal | Email Reminder |
Surveys are routinely used to gather primary data in environmental research. The units to be observed are often randomly selected from a finite population whose main feature is to be geo-referenced and, thus, its spatial distribution has been widely used as a crucial information in designing the sample. In particular the interest is focused on probability samples that are well spread over the population in every dimension which in recent literature are defined as spatially balanced samples. In the last decades this characteristic has become so specific that several sampling algorithms aimed to achieve it were suggested by researchers and survey practitioners. Surprisingly it is mainly based on intuitive considerations and it is not so clear when and to what extent it could have an impact on the efficiency of the estimates. Besides it is also useful to consider that this feature was not so properly defined and, as a consequence, there is a range of possible interpretations that makes unfeasible any comparison between different methods only because they are most likely intended to obtain selected samples with different formal requirements. To approach the problem we used the within sample distance as the summary index of the spatial distribution of a random selection criterion and we tried to realize why and when it should imply a gain in the efficiency of the design. Thus, following this principle, we suggest a method that selects a sample with probability proportional, or more than proportional, to this measure. The method is very flexible and, even if in its nature it is computationally intensive, it is shown to be a suitable solution even when dealing with high sampling rates and large population frames where the main problem arises from the size of the distance matrix to be evaluated. Moreover numerical comparisons on real and artificial data are made between the relative efficiency, measured with respect to the simple random sampling, of the suggested design and some other classical solutions such as the Generalized Random Tessellation Stratified (GRTS) design used by the US Environmental Protection Agency (EPA).
Keywords: spatial sampling, spatially balanced samples, MCMC, variogram, spatial stratification, Generalized Random Tessellation Stratified design.