Predictive statistics : analysis and inference beyond models
著者
書誌事項
Predictive statistics : analysis and inference beyond models
(Cambridge series on statistical and probabilistic mathematics, 46)
Cambridge University Press, 2018
大学図書館所蔵 全14件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 605-634) and index
内容説明・目次
内容説明
All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.
目次
- Part I. The Predictive View: 1. Why prediction?
- 2. Defining a predictive paradigm
- 3. What about modeling?
- 4. Models and predictors: a bickering couple
- Part II. Established Settings for Prediction: 5. Time series
- 6. Longitudinal data
- 7. Survival analysis
- 8. Nonparametric methods
- 9. Model selection
- Part III. Contemporary Prediction: 10. Blackbox techniques
- 11. Ensemble methods
- 12. The future of prediction
- References
- Index.
「Nielsen BookData」 より