Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression and Multilevel/Hierarchical Models pdf epub mobi txt 電子書 下載2025

出版者:Cambridge University Press
作者:Andrew Gelman
出品人:
頁數:648
译者:
出版時間:2006-12-18
價格:USD 69.99
裝幀:Paperback
isbn號碼:9780521686891
叢書系列:Analytical Methods for Social Research
圖書標籤:
  • 統計 
  • 數學 
  • 模型 
  • statistics 
  • 統計學 
  • 機器學習 
  • 分析 
  •  
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Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http://www.stat.columbia.edu/~gelman/arm/

具體描述

讀後感

評分

A very good guide for HLM. Yet it should be noted that HLM here is based upon Bayesian methods. For data from survey, WinBugs frequently fails. >-<

評分

A very good guide for HLM. Yet it should be noted that HLM here is based upon Bayesian methods. For data from survey, WinBugs frequently fails. >-<

評分

A very good guide for HLM. Yet it should be noted that HLM here is based upon Bayesian methods. For data from survey, WinBugs frequently fails. >-<

評分

A very good guide for HLM. Yet it should be noted that HLM here is based upon Bayesian methods. For data from survey, WinBugs frequently fails. >-<

評分

A very good guide for HLM. Yet it should be noted that HLM here is based upon Bayesian methods. For data from survey, WinBugs frequently fails. >-<

用戶評價

评分

有點難讀啊。。沒有 Trochim寫的書通俗易懂

评分

36-663 Hierarchical/Bayesian/Multilevel Models 開啓瞭新世界的大門

评分

算是multilevel裏麵比較簡單的瞭。 另外作者開發的stan或許是未來貝葉思計算統計的必備軟件,可能會取代BUGS

评分

算是multilevel裏麵比較簡單的瞭。 另外作者開發的stan或許是未來貝葉思計算統計的必備軟件,可能會取代BUGS

评分

作者主要是用R with winbugs來寫代碼,作為用Matlab的來說,講到的理論比較淺顯(要做分層模型還是他的BDA更好)。

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