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# Read * Wavelet Methods in Statistics with R (Use R!) by Guy Nason ï eBook or Kindle ePUB. Wavelet Methods in Statistics with R (Use R!) This book contains information on how to tackle many important problems using a multiscale statistical approach. It focuses on how to use multiscale methods and discusses methodological and applied considerations.]
| Title | : | Wavelet Methods in Statistics with R (Use R!) |
| Author | : | |
| Rating | : | 4.63 (936 Votes) |
| Asin | : | 0387759603 |
| Format Type | : | paperback |
| Number of Pages | : | 259 Pages |
| Publish Date | : | 2016-09-25 |
| Language | : | English |
Three Stars Kattamuri S. Sarma It's OK. "Excellent Introduction" according to Heraclitus Junior. This book is an excellent introduction to wavelet techniques in statistics. It takes a very heavily applied approach and sometimes reads like a set of different tutorials on accomplishing certain tasks in R with the author's own wavethresh package. In truth, one can almost think of it as a very detailed manual. This may sound like a criticism but it is in fact one of the book's greatest strengths. There are already plenty of treatments out there on the theory of wavelets and their applications in a variety of fields ranging from the very mathematical to the heavily applied.However, apart from this book, I have been unable to fi. Wavelets in R This is an interesting book, but is very condensed and suffers (for the non-expert) from a lack of intuitive reasoning on why we are doing what and when. Then there is the R-package itself. The limitations of dyadic sets of data are apparent when one comes, for example, to time-series analysis. Yet, the R-software in "wavethresh" for non-decimated wavelets seems to restrict the user to dyadic data and to full decomposition rather than partial decomposition, and cannot handle "reflection" rather than "periodic" boundaries. (According to Percival and Walden (2000) these relaxations or adjustments are some of the main advantages o
This book contains information on how to tackle many important problems using a multiscale statistical approach. It focuses on how to use multiscale methods and discusses methodological and applied considerations.
In conclusion, this is a very welcome book.” (Tom Burr and Claire Longo, Technometrics, Vol. 64, No. The text is interspersed with snippets of R code to illustrate the techniques presented and prove s the basis of an excellent text for private study. The author asks two pertinent questions: Why use wavelets? And Why use wavelets in statistics? For which he proceeds to provide answers; together with illustrative examples of the main uses of wavelets. … the book will be a useful introduction to the methods for the reasonably mathematically competent reader, and it should serve as a guide to some statistical appl
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