Approximate copula-based estimation and prediction of discrete spatial data

作者：Kazianka Hannes^*

来源：Stochastic Environmental Research and Risk Assessment, 2013, 27(8): 2015-2026.

DOI：10.1007/s00477-013-0737-7

• Copula
• Discrete data
• Spatial modeling
• Parameter estimation
• Interpolation

The present paper reports on the use of copula functions to describe the distribution of discrete spatial data, e.g. count data from environmental mapping or areal data analysis. In particular, we consider approaches to parameter point estimation and propose a fast method to perform approximate spatial prediction in copula-based spatial models with discrete marginal distributions. We assess the goodness of the resulting parameter estimates and predictors under different spatial settings and guide the analyst on which approach to apply for the data at hand. Finally, we illustrate the methodology by analyzing the well-known Lansing Woods data set. Software that implements the methods proposed in this paper is freely available in Matlab language on the author's website.

• 出版日期2013-12