用回归与多层模型做数据分析
Data analysis using regression and multilevel/hierarchical models
Andrew Gelman · 初版 2006 年
10人推荐
12位读者提及
13次提及
为什么值得读
- 书名看似晦涩但内容实际极其通俗好懂
- 包含大量真实数据集的实操案例与质检方法
- 是学习R语言与BUGS建模的绝佳教程
Data Analysis Using Regression and Multilevel/Hierarchical Models 是一本为应用研究人员撰写的综合实用指南，旨在指导读者运用线性和非线性回归以及多层模型开展数据分析。本书介绍了丰富多样的模型，同时教授读者如何使用现有的软件包进行拟合。书中通过大量源自作者自身应用研究的真实数据案例来阐释这些概念，并为每个案例提供了配套的编程代码。涵盖的课题包括因果推断、不完全
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……纯粹贝叶斯推理的［1］（以及如何解决它们）。当别人问我统计学从哪儿入门时，我推荐的两本书全都是Gelman写的：《Teaching Statistics: A Bag of Tricks》和《Data Analysis Using Regression and Multilevel/Hierarchical Models》。两本书的书名都极其劝退，但实际上好读得不可思议。Gelman擅长很多事情，但取一个抓人眼球的书名绝对不在此列。如果你有兴趣了解……
…of pure Bayesian reasoning[1] (and how to address them). When people ask me where to get started with statistics, both of the books I recommend are Gelman's: Teaching Statistics: A Bag of Tricks and Data Analysis Using Regression and Multilevel/Hierarchical Models. Both have tremendously off-putting titles, but they're actually incredibly accessible. Gelman is great at many things, but picking sexy titles is not one. If you're interested in understanding the…
……复现研究的案例，只不过把指标优化换成了全方位的多元指标加上定性核查。如果你想读读如何在真实数据集上实操这些内容的好书，我推荐Gelman等人的《Bayesian Data Analysis》以及Gelman和Hill合著的《Data Analysis Using Regression and Multilevel/Hierarchical Models》。市面上绝对还需要一本类似的书，专门聚焦NLP、计算机……等更垂直领域的模型。
…studies of reproducing research but with metric optimization swapped out for a holistic variety of metrics plus qualitative checking. I recommend the books Bayesian Data Analysis by Gelman et al and Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman and Hill if you want to read good accounts of doing this in practice with real data sets. There’s definitely room for a book like this that focuses on more domain specific models in NLP, computer…
……Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl... - Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/ - Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill): https://www.amazon.ca/gp/product/052168689X/ - Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson): https://www.amazon.ca/gp/product/1493926136/ - Reinforcement…
…Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl... - Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/ - Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill): https://www.amazon.ca/gp/product/052168689X/ - Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson): https://www.amazon.ca/gp/product/1493926136/ - Reinforcement…
我读过的最好的R语言入门书是Gelman和Hill写的《Data Analysis Using Regression and Multilevel/Hierarchical Models》（http://www.stat.columbia.edu/~gelman/arm/）。比起基础概率，它更侧重于回归和贝叶斯方法，但书写得非常棒，而且定价极具竞争力（只要39.99美元）。
The best first book on R I've read is Gelman and Hill's "Data Analysis Using Regression and Multilevel/Hierarchical Models" (http://www.stat.columbia.edu/~gelman/arm/). It's more focused on regression and Bayesian methods than basic probability, but it's a great book that's priced very competitively (only $39.99).
Gelman和Hill合著的那本书非常精彩，而且被严重低估了。我猜是它那拗口的书名（Data Analysis Using Regression and Multilevel/Hierarchical Models）掩盖了这样一个事实：它其实是一本入门教材，能带读者从零基础一路学到构建复杂的贝叶斯模型。另外，它还是一本相当不错的R和BUGS教程。
Gelman and Hill is a wonderful and under-rated book. My guess is that its clumsy title (Data Analysis Using Regression and Multilevel/Hierarchical Models) hides the fact that it's an introductory textbook that takes the reader from knowing nothing to eventually constructing complex Bayesian models. Plus, it's a pretty good tutorial on R and BUGS.
……可能更适合先啃下来。如果你本身没有扎实的统计学背景，Hernan/Robins那本书里探讨的许多问题你甚至都看不懂。Gelman/Hill的《Data Analysis Using Regression and Multilevel/Hierarchical Models》或者Angrist/Pischke的《Mastering 'Metrics》把因果推断的思想和技巧融入到了更广泛的回归建模语境中，这使得这些书能立竿见影地派上用场。那些可能……
…probably want to tackle first. Many of the concerns addressed in the Hernan/Robins book probably wouldn't even make sense to you if you didn't have a firm statistical background already. Gelman/Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models or Angrist/Pischke's Mastering 'Metrics embed ideas and techniques from causal inference into the broader context of regression modeling which makes these books more immediately useful. Those would probably…