The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study. Simon N. Wood is a professor of Statistical Science at the University of Bristol, UK, and author of the R package mgcv.
Core Statistics is a compact starter course on the theory, models, and computational tools needed to make informed use of powerful statistical methods.
The stated aims of the Lecture Notes in Biomathematics allow for work that is "unfinished or tentative". This volume is offered in that spirit. The problem addressed is one of the classics of statistical ecology, the estimation of mortality rates from stage-frequency data, but in tackling it we found ourselves making use of ideas and techniques very different from those we expected to use, and in which we had no previous experience. Specifically we drifted towards consideration of some rather specific curve and surface fitting and smoothing techniques. We think we have made some progress (otherwise why publish?), but are acutely aware of the conceptual and statistical clumsiness of parts of the work. Readers with sufficient expertise to be offended should regard the monograph as a challenge to do better. The central theme in this book is a somewhat complex algorithm for mortality estimation (detailed at the end of Chapter 4). Because of its complexity, the job of implementing the method is intimidating. Any reader interested in using the methods may obtain copies of our code as follows: Intelligible Structured Code 1. Hutchinson and deHoog's algorithm for fitting smoothing splines by cross validation 2. Cubic covariant area-approximating splines 3. Cubic interpolating splines 4. Cubic area matching splines 5. Hyman's algorithm for monotonic interpolation based on cubic splines. Prototype User-Hostile Code 6. Positive constrained interpolation 7. Positive constrained area matching 8. The "full method" from chapter 4 9. The "simpler" method from chapter 4.
The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study. Simon N. Wood is a professor of Statistical Science at the University of Bristol, UK, and author of the R package mgcv.
In this challenging and illustrated study, first published in 1990, Simon Varey relates the idea of space in the major novels of Defoe, Fielding and Richardson to its use in the theory and practice of eighteenth-century architecture. Concepts of divine design, expressed in the work of philosophers and theologians, introduced an ideological element to the notion of space which gave it a heightened significance in contemporary thought. Professor Varey's central argument is that space becomes a political instrument used to establish conformity, assert power and give form to the aspirations of social classes. He draws on a wide range of architectural books, both English and European, and on the example of Bath (focusing in particular on its chief architect in the eighteenth century, John Wood). The discussion of novels such as Robinson Crusoe, Tom Jones and Clarissa examines narrative as a form of spatial design, the use of architectural imagery to describe people, and the political control of social space.
Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.
Core Statistics is a compact starter course on the theory, models, and computational tools needed to make informed use of powerful statistical methods.
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