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/
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. >-<
评分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. >-<
评分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. >-<
评分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. >-<
评分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. >-<
算是multilevel里面比较简单的了。 另外作者开发的stan或许是未来贝叶思计算统计的必备软件,可能会取代BUGS
评分作者主要是用R with winbugs来写代码,作为用Matlab的来说,讲到的理论比较浅显(要做分层模型还是他的BDA更好)。
评分PS 733: Maximum Likelihood Estimation
评分Andrew Gelman Regression Hierarchical Models
评分有点难读啊。。没有 Trochim写的书通俗易懂
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