Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

DeepDyve requires Javascript to function. Please enable Javascript on your browser to continue.

Model-based Geostatistics

Part of the Springer Series in Statistics Book Series

DOI: 10.1007/978-0-387-48536-2

ISBN: 978-0-387-32907-9

Publisher: Springer New York - Springer Journals

Jan 1, 2007

LitStream

Chapter 1Introduction

Pages: 1 –26

[The term spatial statistics is used to describe a wide range of statistical models and methods intended for the analysis of spatially referenced data. Cressie (1993) provides a general overview. Within spatial statistics, the term geostatistics refers to models and methods for data with the...
LitStream

Chapter 2An overview of model-based geostatistics

Pages: 27 –45

[The aim of this chapter is to provide a short overview of model-based geostatistics, using the elevation data of Example 1.1 to motivate the various stages in the analysis. Although this example is very limited from a scientific point of view, its simplicity makes it well suited to the task in...
LitStream

Chapter 3Gaussian models for geostatistical data

Pages: 46 –78

[Gaussian stochastic processes are widely used in practice as models for geostatistical data. These models rarely have any physical justification. Rather, they are used as convenient empirical models which can capture a wide range of spatial behaviour according to the specification of their...
LitStream

Chapter 4Generalized linear models for geostatistical data

Pages: 79 –98

[In the classical setting of independently replicated data, the generalized linear model (GLM) as introduced by Nelder and Wedderburn (1972) provides a unifying framework for regression modelling of continuous or discrete data. The original formulation has since been extended, in various ways, to...
LitStream

Chapter 5Classical parameter estimation

Pages: 99 –133

[In this chapter, we discuss methods for formulating a suitable geostatistical model and estimating its parameters. We use the description “classical” in two different senses: firstly, as a reference to the variogram-based methods of estimation which are widely used in classical geostatistics as...
LitStream

Chapter 6Spatial prediction

Pages: 134 –156

[In this chapter, we consider the problem of using the available data to predict aspects of the realised, but unobserved, signal S(·). More formally, our target for prediction is the realised value of a random variable T = T (S), where S denotes the complete set of realised values of S(x) as x...
LitStream

Chapter 7Bayesian inference

Pages: 157 –198

[In Chapters 5 and 6 we discussed geostatistical inference from a classical or non-Bayesian perspective, treating parameter estimation and prediction as separate problems. We did this for two reasons, one philosophical the other practical. Firstly, in the non-Bayesian setting, there is a...
LitStream

Chapter 8Geostatistical design

Pages: 199 –212

[In this chapter, we consider the specific design problem of where to locate the sample points xi: i = 1, ..., n. In particular applications other design issues, such as what to measure at each location, what covariates to record and so forth, may be at least as important as the location of the...

results per page

Prev
1
Next

Browse All eBooks