Multivariate Geostatistics

By Hans Wackernagel. 2004

Chapter 9, covering the comparison of proportions from several samples, has new sections on logit models for ordered outcomes and on the effect of randomness, in which Bayesian methods are discussed. Chapter 10, on combining evidence from fourfold tables, has new material on meta-analysis and largesparse data structures. Much of the remainder of the book is new. Chapter 11 explores logistic regression and includes polytomous logistic regression. Chapter 12 covers Poisson regression, including a discussion of overdispersion. Chapter 13, on the analysis of data from matched samples, was formerly Chapter 8 in the 2E. It is followed by a chapter on regression methods for matched samples, which includes parametric modeling and conditional logistic regression. Chapters 15 and 16 are also new. Chapter 15, “Analysis of Correlated Binary Data,” provides methods for longitudinal data, and Chapter 16, “Missing Data,” considers situations in which the data are incomplete. Chapters 17–19, on misclassi cation, measurement of interrater agreement, and standardization of rates, are assembled from the last four chapters of the 2E. Each chapter contains some new material. Upgrading a book originally based on a desk calculator to a book that is appropriate for the modern computing environment obviously would be a challenge for any new authors. The authors have not attempted to complete the entire task. There is no direct application of statistical software, such as SAS or S-PLUS, in this book. Nevertheless, anyone who still uses the 2E would surely want a copy of this new edition. Likewise, persons who regularly encounter this type of data would certainly want this book available as one of their desktop references.

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Citations in the corpus

Kernels for Vector-Valued Functions: a Review (2011, background): “In a regression setting, one idea is to estimate B in a separate inference step as the covariance matrix of the output vectors in the training set and this is standard in the geostatistics literature [104].”
Multi-fidelity Gaussian process regression for computer experiments (2013, background): “…by [Krige, 1951] to interpolate a random field at unobserved locations (see [Matheron, 1963], [Matheron, 1969], [Chilès and Delfiner, 1999], [Wackernagel, 2003], [Berger et al., 2001] and [Gneiting et al., 2010]) and it has been developed in many areas such as environmental and atmospheric…”
A progressive morphological filter for removing nonground measurements from airborne LIDAR data (2003, methodology): “unfiltered and filtered LIDAR data using Kriging interpolation [15].”
Sequential Data Assimilation Techniques in Oceanography (2003, background): “We point towards von Storch & Zwiers (1999) for illustrations of the use of EOFs in natural sciences and towards Wackernagel (2003) for approaches to compute EOFs of multivariate autocorrelated data using linear coregionalization models.”
A Geostatistical Framework for Area-to-Point Spatial Interpolation (2004, methodology): “Most geostatistical textbooks and applications regarding change of support, however, address only the problem of point-to-area interpolation via the use of block Kriging (Isaaks and Srivastava 1989; Cressie 1993; Wackernagel 1995; Goovaerts 1997).”
Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity (2020, methodology): “…the transformation of the data into values that are compatible with a Gaussian process or another tractable process (Chilès & Delfiner, 2009; Wackernagel, 2003) and (2) methods assuming that the random field follows specific processes, such as χ2 or t‐Student (e.g., Adler & Taylor, 2007),…”
Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2015, background): “The main were reviewed in Gamerman et al. (2007) and include independent GPs, dependent processes with a common correlation function or linear mixtures of independent GPs, the co-regionalization models described by Wackernagel (2003).”
Geometric harmonics: A novel tool for multiscale out-of-sample extension of empirical functions (2006, methodology): “Our work is also highly related to the Kriging technique widely employed in geostatistics [5].”
Combining geostatistics and Kalman filtering for data assimilation in an estuarine system (2002, background): “As a function of space, qn is generally assumed stationary of order 2 which means that, in addition to the zero mean assumption above, its covariance function C(x;h) = cov(qn(x+ h);qn(x)) has to be translation independent 8(x;h), C(x;h) = C(h) here written in spatial continuous notations, so that it”
Max-stable processes for modelling extremes observed in space and time (2011, methodology): “For a detailed introduction, we refer to Wackernagel [43], Chapter 9.”
Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom (2009, unknown): “With KED, a secondary variable is used which describes the average shape of the primary variable (Wackernagel, 2003) and the trend-free variogram is required (here, the trend being modelled as a function of elevation).”
On digital soil mapping (2003, methodology): “First , the param - eters of all the [ ( q + 1 ) q ] / 2 variograms and cross - variograms have to be estimated and the parameters have to obey a strict inequality ( Wackernagel , 1987 ) .”
Probabilistic uncertainty analysis of an FRF of a structure using a Gaussian process emulator (2011, methodology): “Here, we use a method based on the linear model of coregionalization (LMC), a tool that is popular in geostatistics for modelling multivariate spatial processes (Journel and Huijbregts, 1978; Wackernagel, 1995; Goulard and Voltz, 1992; Gelfand et al., 2004).”
Detecting dependence between marks and locations of marked point processes (2004, methodology): “This is a special case of the random-field model, with a pure nugget effect for the correlation structure of the random field (Wackernagel, 1998).”
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling (2007, background): “Much of modern engineering design is now done through computer modeling, which is both faster and more cost-eﬀective than building small-scale models, particularly in the earlier stages of design when more scope for changes is desired.”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “The TOPAZ system has been set in near real-time forecasting mode in January 2003, initially using the perturbed observations EnKF (Evensen, 2003) (c.f., Section 4.1), and then moved on to the DEnKF (Sakov & Oke, 2008b) (c.f., Section 4.2) in January 2010.”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, methodology): “This technique is well known in geostatistics (Wackernagel 2003).”
Community ecology in the age of multivariate multiscale spatial analysis (2012, methodology): “The scale components of a nested variogram model, which directly relate to the ranges of the individual variogram models, can then be extracted and mapped by filter kriging, which explicitly decomposes the hierarchical layering of the spatial structures identified in the data (Wackernagel 2003).”