プライム無料体験をお試しいただけます
プライム無料体験で、この注文から無料配送特典をご利用いただけます。
非会員 | プライム会員 | |
---|---|---|
通常配送 | ¥410 - ¥450* | 無料 |
お急ぎ便 | ¥510 - ¥550 | |
お届け日時指定便 | ¥510 - ¥650 |
*Amazon.co.jp発送商品の注文額 ¥3,500以上は非会員も無料
無料体験はいつでもキャンセルできます。30日のプライム無料体験をぜひお試しください。
無料のKindleアプリをダウンロードして、スマートフォン、タブレット、またはコンピューターで今すぐKindle本を読むことができます。Kindleデバイスは必要ありません。
ウェブ版Kindleなら、お使いのブラウザですぐにお読みいただけます。
携帯電話のカメラを使用する - 以下のコードをスキャンし、Kindleアプリをダウンロードしてください。
何か問題が発生しました。後で再度リクエストしてください。
OK
Dynamic Linear Models with R (Use R!) ペーパーバック – イラスト付き, 2009/6/2
{"desktop_buybox_group_1":[{"displayPrice":"¥13,455","priceAmount":13455.00,"currencySymbol":"¥","integerValue":"13,455","decimalSeparator":null,"fractionalValue":null,"symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"I8%2FcVSTeRgi%2FcszK3cvrTj%2FQfMps2y%2BFHmdzKbo7mECUW3u9aE3fbjVLeJtErjyx70JgTU1QbDCQsKXRTqMljSnpqLeRoq4z40AxAlNxYF2MloNol2wA%2By2%2BvhWvIWlI","locale":"ja-JP","buyingOptionType":"NEW","aapiBuyingOptionIndex":0}, {"displayPrice":"¥11,162","priceAmount":11162.00,"currencySymbol":"¥","integerValue":"11,162","decimalSeparator":null,"fractionalValue":null,"symbolPosition":"left","hasSpace":false,"showFractionalPartIfEmpty":true,"offerListingId":"I8%2FcVSTeRgi%2FcszK3cvrTj%2FQfMps2y%2BFLM8pPF8gvZt9u9HghslGZeo5Jv8Cmt%2FPVCBSPh%2Bioo9hRPSjEEqcqEEJkToV1HDeFl7gcx0lzS4HdTVVHZ6KWi1NEFnsugHVUKRbWXxjnAuzgdA9zsgQiDSzGjeriS6k%2Bc1lM0ZT1uKoWEqXsbUNaw%3D%3D","locale":"ja-JP","buyingOptionType":"USED","aapiBuyingOptionIndex":1}]}
購入オプションとあわせ買い
This text introduces general state space models in detail before focusing on dynamic linear models, emphasizing their Bayesian analysis. It illustrates all the fundamental steps needed to use dynamic linear models in practice, using R.
- 本の長さ268ページ
- 言語英語
- 出版社Springer
- 発売日2009/6/2
- 寸法15.49 x 1.55 x 23.5 cm
- ISBN-100387772375
- ISBN-13978-0387772370
商品の説明
レビュー
the Use R! series for providing a valuable collection of books for a fantastic open-source software.” (American Statistician, August 2010, Vol. 64, No. 3)
登録情報
- 出版社 : Springer; 第2009版 (2009/6/2)
- 発売日 : 2009/6/2
- 言語 : 英語
- ペーパーバック : 268ページ
- ISBN-10 : 0387772375
- ISBN-13 : 978-0387772370
- 寸法 : 15.49 x 1.55 x 23.5 cm
- Amazon 売れ筋ランキング: - 293,385位洋書 (洋書の売れ筋ランキングを見る)
- - 1,677位Probability & Statistics
- - 2,793位Professional Applied Mathematics
- - 63,311位Education & Reference
- カスタマーレビュー:
著者について
著者をフォローして、新作のアップデートや改善されたおすすめを入手してください。
著者の本をもっと発見したり、よく似た著者を見つけたり、著者のブログを読んだりしましょう
他の国からのトップレビュー
staurus
5つ星のうち5.0
Five Stars
2016年6月17日に英国でレビュー済みAmazonで購入
Good book. Excellent service.
Charles L.
5つ星のうち5.0
must have for state space/dynamic linear modeling
2014年4月22日にアメリカ合衆国でレビュー済みAmazonで購入
I did use this book for the last 4 years.
I believe that the quality of this book must be appreciated in context. As one reviewer stated (and I fully support that) - this is a very good description of how to apply the dlm package with well-selected examples. Its strengths are: (1) very well written package - I did learn some new things about R while analyzing the code; (2) good examples illustrating finer points of dynamic modeling in multiple contexts; (3) clear though sometimes terse explanations of the overall field.
Since this is the best book on application I have found (through the years) it definitely deserves 5 stars. That does not mean it is perfect for everybody.
I disagree with the 3-star review [with the exception of 'The software package is itself very powerful end elegantly implemented' :-)].
This book is not be-all-end-all and it does not attempt to be. The theoretical basis and numerous - really numerous - and well explained practical examples are contained in 680 pages of 'Bayesian Forecasting and Dynamic Models' by West and Harrison. More recent 'Time Series: Modeling, Computation, and Inference ' by Prado and West contains plenty of explanations using similar methods and gives a good update on theory. There are many other books, though I found 'Time Series Analysis by State Space Methods' by Durbin and Koopman (2001 version) rather dry and tough going for somebody without earlier experience in this area.
I think that the learning curve is slightly sharper in state space than in more traditional ARIMA-based approach. Also, the more traditional approach has simply many more books published on various level, most of that introductory. Still, if you want to use better version of modeling that is a small price you have to pay.
Possibly some will expect this book to be more like (excellent in that area) 'An Introduction to Analysis of Financial Data with R' by Ruey Tsay which can be 'consumed' without much external reading and there is plenty of R examples to illustrate most of the element of the traditional approach. However, note that Tsay's book has an _introduction_ in the title, thus different audience. Additional advantage is the limitation of the topic to the financial time series - while 'Dynamic Linear Model with R' are for multitude of application areas.
Personally I wish the authors found time to create a second edition of this book with some updates to the methods etc. - though I do appreciate that the market for such books is small. For general state space and dynamic modeling field a book similar in approach to Tsay's 'An Introduction to Analysis of Financial Data with R' could result in wider use of that approach.
I believe that the quality of this book must be appreciated in context. As one reviewer stated (and I fully support that) - this is a very good description of how to apply the dlm package with well-selected examples. Its strengths are: (1) very well written package - I did learn some new things about R while analyzing the code; (2) good examples illustrating finer points of dynamic modeling in multiple contexts; (3) clear though sometimes terse explanations of the overall field.
Since this is the best book on application I have found (through the years) it definitely deserves 5 stars. That does not mean it is perfect for everybody.
I disagree with the 3-star review [with the exception of 'The software package is itself very powerful end elegantly implemented' :-)].
This book is not be-all-end-all and it does not attempt to be. The theoretical basis and numerous - really numerous - and well explained practical examples are contained in 680 pages of 'Bayesian Forecasting and Dynamic Models' by West and Harrison. More recent 'Time Series: Modeling, Computation, and Inference ' by Prado and West contains plenty of explanations using similar methods and gives a good update on theory. There are many other books, though I found 'Time Series Analysis by State Space Methods' by Durbin and Koopman (2001 version) rather dry and tough going for somebody without earlier experience in this area.
I think that the learning curve is slightly sharper in state space than in more traditional ARIMA-based approach. Also, the more traditional approach has simply many more books published on various level, most of that introductory. Still, if you want to use better version of modeling that is a small price you have to pay.
Possibly some will expect this book to be more like (excellent in that area) 'An Introduction to Analysis of Financial Data with R' by Ruey Tsay which can be 'consumed' without much external reading and there is plenty of R examples to illustrate most of the element of the traditional approach. However, note that Tsay's book has an _introduction_ in the title, thus different audience. Additional advantage is the limitation of the topic to the financial time series - while 'Dynamic Linear Model with R' are for multitude of application areas.
Personally I wish the authors found time to create a second edition of this book with some updates to the methods etc. - though I do appreciate that the market for such books is small. For general state space and dynamic modeling field a book similar in approach to Tsay's 'An Introduction to Analysis of Financial Data with R' could result in wider use of that approach.
Kevin Riggle
5つ星のうち4.0
Useful book so far; Kindle edition has OCR issues
2015年3月13日にアメリカ合衆国でレビュー済みAmazonで購入
The book has been useful so far; I'm only partway in so I can't comment on everything, but so far it's a good overview of the math behind many of the more complicated modeling and predictive techniques being used today.
Unfortunately the Kindle edition suffers from occasional but significant OCR issues in the math in the running text, and for a mathematical book that's a killer -- *all* the formatting needs to be preserved *all* the time, or the equations and their explanation don't make sense. Learn from my mistakes and save yourself the $60: buy the paper version *first*.
Unfortunately the Kindle edition suffers from occasional but significant OCR issues in the math in the running text, and for a mathematical book that's a killer -- *all* the formatting needs to be preserved *all* the time, or the equations and their explanation don't make sense. Learn from my mistakes and save yourself the $60: buy the paper version *first*.
Cliente de Amazon
5つ星のうち5.0
Clear and to the point
2009年11月12日にアメリカ合衆国でレビュー済みAmazonで購入
This book might be seen as extended documentation for the R package dlm, authored by Petris. It is in fact more than that. It discusses clearly the theory foundations from a Bayesian point of view, even if the length of the book implies that the discussion has to be terse at times. R code and examples are interspersed with the theory, greatly enhancing reader understanding.
This is a good buy for any applied statistician learning or using state-space models.
Although it stands on its own, I think many readers would like to have at times recourse to lengthier books such as Time Series Analysis by State Space Methods (Oxford Statistical Science Series) or Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) . Readers unfamiliar with R should read first one of the many good introductions in existence, such as Introductory Statistics with R (Statistics and Computing) , Modern Applied Statistics with S or Probability and Statistics with R , to name a few.
All in all, a welcomed addition to time series literature in R and a valuable complement to an outstanding R package.
This is a good buy for any applied statistician learning or using state-space models.
Although it stands on its own, I think many readers would like to have at times recourse to lengthier books such as Time Series Analysis by State Space Methods (Oxford Statistical Science Series) or Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) . Readers unfamiliar with R should read first one of the many good introductions in existence, such as Introductory Statistics with R (Statistics and Computing) , Modern Applied Statistics with S or Probability and Statistics with R , to name a few.
All in all, a welcomed addition to time series literature in R and a valuable complement to an outstanding R package.
Richard
5つ星のうち5.0
Good implementation of Bayesian DLMs in R
2022年4月12日にアメリカ合衆国でレビュー済みAmazonで購入
The only problem is that the book website no longer seems available, making some example data and other items (some R scripts by the authors) hard to find.