Formal parameter estimation and model identification procedures for continuous domain spatial processes are introduced. The processes are assumed to be adequately described by a linear model with residuals that follow a second-order stationary Gaussian random field and data are assumed to consist of noisy observations of the process at arbitrary sampling locations. A general class of two-dimensional rational spectral density functions with elliptic contours is used to model the spatial covariance function. An iterative estimation procedure alleviates many of the computational difficulties of conventional maximum likelihood estimation for non-lattice data. The procedure is applied to several generated data sets and to an actual ground-water data set.
Series B (Statistical Methodology) of the Journal of the Royal Statistical Society started out simply as the Supplement to the Journal of the Royal Statistical Society in the Society's centenary year of 1934. The journal now publishes high quality papers on the methodological aspects of statistics. The objective of papers is to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods. JSTOR provides a digital archive of the print version of Journal of the Royal Statistical Society, Series B: Statistical Methodology. The electronic version of Journal of the Royal Statistical Society, Series B: Statistical Methodology is available at http://www.blackwell-synergy.com/servlet/useragent?func=showIssues&code;=rssb. Authorized users may be able to access the full text articles at this site.
Wiley is a global provider of content and content-enabled workflow solutions in areas of scientific, technical, medical, and scholarly research; professional development; and education. Our core businesses produce scientific, technical, medical, and scholarly journals, reference works, books, database services, and advertising; professional books, subscription products, certification and training services and online applications; and education content and services including integrated online teaching and learning resources for undergraduate and graduate students and lifelong learners. Founded in 1807, John Wiley & Sons, Inc. has been a valued source of information and understanding for more than 200 years, helping people around the world meet their needs and fulfill their aspirations. Wiley has published the works of more than 450 Nobel laureates in all categories: Literature, Economics, Physiology or Medicine, Physics, Chemistry, and Peace. Wiley has partnerships with many of the world’s leading societies and publishes over 1,500 peer-reviewed journals and 1,500+ new books annually in print and online, as well as databases, major reference works and laboratory protocols in STMS subjects. With a growing open access offering, Wiley is committed to the widest possible dissemination of and access to the content we publish and supports all sustainable models of access. Our online platform, Wiley Online Library (wileyonlinelibrary.com) is one of the world’s most extensive multidisciplinary collections of online resources, covering life, health, social and physical sciences, and humanities.
This item is part of JSTOR collection
For terms and use, please refer to our Terms and Conditions
Journal of the Royal Statistical Society. Series B (Methodological)
© 1988 Royal Statistical Society
Request Permissions
