andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-183 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
sentIndex sentText sentNum sentScore
1 A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? [sent-1, score-1.196]
2 My reply: I’m not up-to-date on the Bayesian econometrics literature. [sent-3, score-0.258]
3 TTony Lancaster came out with a book a few years ago that might have some of these models. [sent-4, score-0.528]
4 Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there. [sent-6, score-1.045]
wordName wordTfidf (topN-words)
[('simultaneous', 0.346), ('bayesian', 0.328), ('inherently', 0.278), ('equation', 0.266), ('econometrics', 0.258), ('latent', 0.254), ('econ', 0.251), ('systems', 0.233), ('suggestions', 0.209), ('asks', 0.204), ('commenters', 0.198), ('seeing', 0.183), ('suggest', 0.177), ('parameters', 0.169), ('literature', 0.156), ('approach', 0.134), ('came', 0.132), ('reply', 0.122), ('ago', 0.12), ('book', 0.11), ('models', 0.105), ('lot', 0.093), ('maybe', 0.093), ('years', 0.091), ('seems', 0.086), ('might', 0.075), ('could', 0.061), ('like', 0.046), ('think', 0.046)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 183 andrew gelman stats-2010-08-04-Bayesian models for simultaneous equation systems?
Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
Introduction: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
3 0.17440018 1554 andrew gelman stats-2012-10-31-It not necessary that Bayesian methods conform to the likelihood principle
Introduction: Bayesian inference, conditional on the model and data, conforms to the likelihood principle. But there is more to Bayesian methods than Bayesian inference. See chapters 6 and 7 of Bayesian Data Analysis for much discussion of this point. It saddens me to see that people are still confused on this issue.
4 0.16213563 723 andrew gelman stats-2011-05-21-Literary blurb translation guide
Introduction: “Just like literature, only smaller.”
5 0.15074694 2368 andrew gelman stats-2014-06-11-Bayes in the research conversation
Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
6 0.14657754 1205 andrew gelman stats-2012-03-09-Coming to agreement on philosophy of statistics
7 0.14496867 1712 andrew gelman stats-2013-02-07-Philosophy and the practice of Bayesian statistics (with all the discussions!)
8 0.13724536 205 andrew gelman stats-2010-08-13-Arnold Zellner
9 0.13457155 1228 andrew gelman stats-2012-03-25-Continuous variables in Bayesian networks
10 0.13399108 449 andrew gelman stats-2010-12-04-Generalized Method of Moments, whatever that is
11 0.12815808 534 andrew gelman stats-2011-01-24-Bayes at the end
12 0.12399031 1469 andrew gelman stats-2012-08-25-Ways of knowing
13 0.12147438 1610 andrew gelman stats-2012-12-06-Yes, checking calibration of probability forecasts is part of Bayesian statistics
14 0.11811724 1695 andrew gelman stats-2013-01-28-Economists argue about Bayes
15 0.11701541 117 andrew gelman stats-2010-06-29-Ya don’t know Bayes, Jack
16 0.1169931 2033 andrew gelman stats-2013-09-23-More on Bayesian methods and multilevel modeling
17 0.11672577 1719 andrew gelman stats-2013-02-11-Why waste time philosophizing?
18 0.11430088 1912 andrew gelman stats-2013-06-24-Bayesian quality control?
19 0.11294131 114 andrew gelman stats-2010-06-28-More on Bayesian deduction-induction
20 0.11268599 1779 andrew gelman stats-2013-03-27-“Two Dogmas of Strong Objective Bayesianism”
topicId topicWeight
[(0, 0.143), (1, 0.129), (2, -0.086), (3, 0.093), (4, -0.094), (5, 0.041), (6, -0.009), (7, 0.037), (8, 0.084), (9, -0.008), (10, 0.066), (11, -0.069), (12, 0.022), (13, 0.043), (14, 0.133), (15, 0.048), (16, 0.016), (17, 0.053), (18, -0.008), (19, 0.02), (20, -0.022), (21, 0.101), (22, 0.017), (23, 0.007), (24, -0.008), (25, -0.043), (26, -0.026), (27, -0.019), (28, -0.002), (29, 0.002), (30, -0.034), (31, -0.001), (32, -0.002), (33, -0.009), (34, -0.012), (35, 0.009), (36, -0.004), (37, 0.033), (38, 0.0), (39, -0.027), (40, -0.033), (41, 0.03), (42, -0.012), (43, 0.016), (44, -0.044), (45, -0.042), (46, 0.057), (47, 0.078), (48, 0.012), (49, -0.03)]
simIndex simValue blogId blogTitle
same-blog 1 0.99275535 183 andrew gelman stats-2010-08-04-Bayesian models for simultaneous equation systems?
Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
Introduction: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
3 0.82962984 2368 andrew gelman stats-2014-06-11-Bayes in the research conversation
Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
4 0.82678759 1497 andrew gelman stats-2012-09-15-Our blog makes connections!
Introduction: Steve Cohen writes: Thank you for fulfilling another request of mine almost two years ago. I gave you a job description of a senior Bayesian statistical software developer that I was loooking to hire. You kindly posted it on your site. THAT EVENING, I received a response from a fellow in Florida who had worked as a C++ programmer for 10+ years and — inexplicably — was now finishing his PhD in Bayesian econometrics. We hired him and he has done magical things with our software, making estimating hierarchical Bayesian models in “real time” on very large, complex datasets a reality. He’s a super guy, to boot! Together with others on our staff, we now have fully parallelized versions of most econometric models working on over 100 cores. (You may have read a paper by one of my partners, Prof John Liechty at Penn State, on parallel slicer samplers).
5 0.82642436 1912 andrew gelman stats-2013-06-24-Bayesian quality control?
Introduction: Gabriel Murray writes: I saw this post and response from about 5 years ago, regarding a fellow analyzing levels of white blood cells. He was asking about Bayesian approaches to quality control and couldn’t find a canonical resource on that topic. Five years on and I similarly don’t see many good resources on the topic, though it seems like a useful application of Bayesian analysis. Just wondering if you have any further thoughts on the matter now after a few years. In my searching, I did find this interesting book on Bayesian Reliability. It’s not quality control, of course, but it does seem that failure prediction and risk analysis have some bearing on quality control and process control. And the cited reviews are very positive. It was written by some researchers at Los Alamos. Also an edited book on process control: I saw that Bill Bolstad gives it a positive review in one of the journals. And a book on Bayesian risk analysis.
6 0.82346565 2000 andrew gelman stats-2013-08-28-Why during the 1950-1960′s did Jerry Cornfield become a Bayesian?
7 0.81839687 1228 andrew gelman stats-2012-03-25-Continuous variables in Bayesian networks
8 0.80538893 1610 andrew gelman stats-2012-12-06-Yes, checking calibration of probability forecasts is part of Bayesian statistics
9 0.8013162 1438 andrew gelman stats-2012-07-31-What is a Bayesian?
10 0.7906751 427 andrew gelman stats-2010-11-23-Bayesian adaptive methods for clinical trials
11 0.78816092 117 andrew gelman stats-2010-06-29-Ya don’t know Bayes, Jack
12 0.77847314 2293 andrew gelman stats-2014-04-16-Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials
13 0.77309263 1259 andrew gelman stats-2012-04-11-How things sound to us, versus how they sound to others
14 0.76801103 1781 andrew gelman stats-2013-03-29-Another Feller theory
15 0.76556623 1554 andrew gelman stats-2012-10-31-It not necessary that Bayesian methods conform to the likelihood principle
16 0.76464462 449 andrew gelman stats-2010-12-04-Generalized Method of Moments, whatever that is
18 0.75520557 534 andrew gelman stats-2011-01-24-Bayes at the end
19 0.74601436 2254 andrew gelman stats-2014-03-18-Those wacky anti-Bayesians used to be intimidating, but now they’re just pathetic
20 0.73431736 1712 andrew gelman stats-2013-02-07-Philosophy and the practice of Bayesian statistics (with all the discussions!)
topicId topicWeight
[(15, 0.128), (24, 0.27), (86, 0.042), (87, 0.15), (99, 0.246)]
simIndex simValue blogId blogTitle
1 0.95787358 233 andrew gelman stats-2010-08-25-Lauryn Hill update
Introduction: Juli thought this might answer some of my questions . To me, though, it seemed a bit of a softball interview, didn’t really go into the theory that the reason she’s stopped recording is that she didn’t really write most of the material herself.
same-blog 2 0.94959158 183 andrew gelman stats-2010-08-04-Bayesian models for simultaneous equation systems?
Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
3 0.91029024 2188 andrew gelman stats-2014-01-27-“Disappointed with your results? Boost your scientific paper”
Introduction: This , sent in by Ben Bolker, is just tooooo funny. Click on the above image to see more clearly. In addition to the quote I used in the above title, there’s also this: +10.000 correlations/min Sooner than later, your future discovery will pop up. and this: The most relevant conclusions in your scientific paper are concealed under the experimental data but you simply cannot see them. All they need is to pipe in a Mechanical Turk request form on one end and a Psychological Science submission form on the other, and they’ll have the complete package!
4 0.90976822 355 andrew gelman stats-2010-10-20-Andy vs. the Ideal Point Model of Voting
Introduction: Last week, as I walked into Andrew’s office for a meeting, he was formulating some misgivings about applying an ideal-point model to budgetary bills in the U.S. Senate. Andrew didn’t like that the model of a senator’s position was an indifference point rather than at their optimal point, and that the effect of moving away from a position was automatically modeled as increasing in one direction and decreasing in the other. Executive Summary The monotonicity of inverse logit entails that the expected vote for a bill among any fixed collection of senators’ ideal points is monotonically increasing (or decreasing) with the bill’s position, with direction determined by the outcome coding. The Ideal-Point Model The ideal-point model’s easy to write down, but hard to reason about because of all the polarity shifting going on. To recapitulate from Gelman and Hill’s Regression book (p. 317), using the U.S. Senate instead of the Supreme Court, and ignoring the dis
5 0.90381324 918 andrew gelman stats-2011-09-21-Avoiding boundary estimates in linear mixed models
Introduction: Pablo Verde sends in this letter he and Daniel Curcio just published in the Journal of Antimicrobial Chemotherapy. They had published a meta-analysis with a boundary estimate which, he said, gave nonsense results. Here’s Curcio and Verde’s key paragraph: The authors [of the study they are criticizing] performed a test of heterogeneity between studies. Given that the test result was not significant at 5%, they decided to pool all the RRs by using a fixed-effect meta-analysis model. Unfortunately, this is a common practice in meta-analysis, which usually leads to very misleading results. First of all, the pooled RR as well as its standard error are sensitive to 2 the estimation of the between-studies standard deviation (SD). SD is difficult to estimate with a small number of studies. On the other hand, it is very well known that the significant test of hetero- geneity lacks statistical power to detect values of SD greater than zero. In addition, the statistically non-significant re
6 0.9019587 1800 andrew gelman stats-2013-04-12-Too tired to mock
10 0.88231277 783 andrew gelman stats-2011-06-30-Don’t stop being a statistician once the analysis is done
11 0.8819297 1062 andrew gelman stats-2011-12-16-Mr. Pearson, meet Mr. Mandelbrot: Detecting Novel Associations in Large Data Sets
12 0.88077521 278 andrew gelman stats-2010-09-15-Advice that might make sense for individuals but is negative-sum overall
13 0.87977308 803 andrew gelman stats-2011-07-14-Subtleties with measurement-error models for the evaluation of wacky claims
15 0.8779636 779 andrew gelman stats-2011-06-25-Avoiding boundary estimates using a prior distribution as regularization
16 0.87766159 197 andrew gelman stats-2010-08-10-The last great essayist?
18 0.87593228 801 andrew gelman stats-2011-07-13-On the half-Cauchy prior for a global scale parameter