Geostatistical Error Management: Quantifying Uncertainty for Environmental Sampling and Mapping
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My only complaint is that, in their attempt to write an all-encompassing textbook, Tamhane and Dunlop all but omit Bayesian inference. The brevity of the section, the lack of interesting practical examples, and the near absence of exercises from that section are less than satisfying considering the everincreasing role of Bayesian methods within statistics. Despite that fact, the authors have done a marvelous job of presenting an enormous amount of material and should be applauded for doing so. They state that one of their goals was to develop both a textbook and a thorough reference book. Tamhane and Dunlop have certainly accomplished that. As the authors note, however, most curricula do not allow for a two-semester course after a prerequisite of probability. As long as that remains true, Statistics and Data Analysis is not likely to be widely adopted. I do, however, hope that I am wrong.
[1] D. Hand,et al. Artificial Intelligence Frontiers in Statistics , 2020 .
[2] Noel A Cressie,et al. Statistics for Spatial Data. , 1992 .