Should Geostatistics Be Model-Based?
Often, there are two streams in statistical research-one developed by practitioners and other by main stream statisticians. Development of geostatistics is a very good example where pioneering work under realistic assumptions came from mining engineers whereas it is only now thai statistical framework is getting more transparent. The subject with statistical emphasis has been evolving, as seen by various excellent books from statistical sides (Banerjee, S., Carlin, B.P. and Gelfand, A.E. 2004. Hierarchical Modelling and Analysis for Spatial Data, Chapman and Hall/CRC, New York; Cressie, N. 1992. Statistics for Spatial Data, Wiley, New York; Diggle, P.J. and Ribeiro Jr., P.J. 2007. Model-Based Geostatistics, Springer, New York; Ripley, B.D. 1981. Spatial Statistics, Wiley, New York; Stein, M.L. 1999. Interpolation of Spatial Data: Some Theory for Kriging, Springer, New York). We will mainly discuss here in detail maximum likelihood methods for spatial linear model (kriging). Some other key topics are briefly outlined, including rock fracture modelling and risk assessment for the safe storage of hazardous wastes in underground repositories.
Kanti V. Mardia
Department of Statistics, University of Leeds, Leeds
国际会议
The 12th Conference of the International Association for Mathematical Geology(第12届国际数学地质大会)
北京
英文
4-9
2007-08-26（万方平台首次上网日期，不代表论文的发表时间）