andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2012 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: American University in Washington, D.C. has two full-time tenure-line positions available : An ideal candidate will have facility with computation, and can identify specific prospects for on-campus collaboration, possibly interdepartmental. We are particularly interested in candidates who can assist colleagues who need to deal with data sets that are too large, distributed, or heterogeneous to be amenable to traditional methods of analysis. From a mathematician, we also seek a research program with deep roots in mathematics. From a statistician, we seek a familiarity with Bayesian modeling. We are open to researchers who ignore traditional disciplinary boundaries. I like the bits about computation, interdepartmental, and Bayesian modeling. My former colleagues at Berkeley would be spinning in their theorems were they to see this (but, don’t worry, I doubt they read blogs). I really do think that computation is more important than theorem-proving. Both are important (for exam
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1 has two full-time tenure-line positions available : An ideal candidate will have facility with computation, and can identify specific prospects for on-campus collaboration, possibly interdepartmental. [sent-3, score-0.717]
2 We are particularly interested in candidates who can assist colleagues who need to deal with data sets that are too large, distributed, or heterogeneous to be amenable to traditional methods of analysis. [sent-4, score-0.926]
3 From a mathematician, we also seek a research program with deep roots in mathematics. [sent-5, score-0.442]
4 From a statistician, we seek a familiarity with Bayesian modeling. [sent-6, score-0.362]
5 We are open to researchers who ignore traditional disciplinary boundaries. [sent-7, score-0.434]
6 I like the bits about computation, interdepartmental, and Bayesian modeling. [sent-8, score-0.11]
7 My former colleagues at Berkeley would be spinning in their theorems were they to see this (but, don’t worry, I doubt they read blogs). [sent-9, score-0.501]
8 I really do think that computation is more important than theorem-proving. [sent-10, score-0.468]
9 Both are important (for example, without theory, there’d be no HMC, no Nuts, and thus no Stan), but if you have to pick something to emphasize, I think computation is the way to go. [sent-11, score-0.468]
10 I have warm feelings about AU because that’s where I took my first college course, over 35 years ago. [sent-12, score-0.225]
11 And, amazingly enough, Mary Gray, a math professor who was very nice to me back then, is still teaching in the math department there! [sent-13, score-0.684]
12 is a great place for statisticians and their department is a friendly place that has become very supportive of faculty research. [sent-16, score-0.746]
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same-blog 1 0.99999988 2012 andrew gelman stats-2013-09-07-Job openings at American University
Introduction: American University in Washington, D.C. has two full-time tenure-line positions available : An ideal candidate will have facility with computation, and can identify specific prospects for on-campus collaboration, possibly interdepartmental. We are particularly interested in candidates who can assist colleagues who need to deal with data sets that are too large, distributed, or heterogeneous to be amenable to traditional methods of analysis. From a mathematician, we also seek a research program with deep roots in mathematics. From a statistician, we seek a familiarity with Bayesian modeling. We are open to researchers who ignore traditional disciplinary boundaries. I like the bits about computation, interdepartmental, and Bayesian modeling. My former colleagues at Berkeley would be spinning in their theorems were they to see this (but, don’t worry, I doubt they read blogs). I really do think that computation is more important than theorem-proving. Both are important (for exam
2 0.19439277 590 andrew gelman stats-2011-02-25-Good introductory book for statistical computation?
Introduction: Geen Tomko asks: Can you recommend a good introductory book for statistical computation? Mostly, something that would help make it easier in collecting and analyzing data from student test scores. I don’t know. Usually, when people ask for a starter statistics book, my recommendation (beyond my own books) is The Statistical Sleuth. But that’s not really a computation book. ARM isn’t really a statistical computation book either. But the statistical computation books that I’ve seen don’t seems so relevant for the analyses that Tomko is looking for. For example, the R book of Venables and Ripley focuses on nonparametric statistics, which is fine but seems a bit esoteric for these purposes. Does anyone have any suggestions?
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Introduction: We interrupt our usual program of Ed Wegman Gregg Easterbrook Niall Ferguson mockery to deliver a serious update on our statistical computing project. Stan (“Sampling Through Adaptive Neighborhoods”) is our new C++ program (written mostly by Bob Carpenter) that draws samples from Bayesian models. Stan can take different sorts of inputs: you can write the model in a Bugs-like syntax and it goes from there, or you can write the log-posterior directly as a C++ function. Most of the computation is done using Hamiltonian Monte Carlo. HMC requires some tuning, so Matt Hoffman up and wrote a new algorithm, Nuts (the “No-U-Turn Sampler”) which optimizes HMC adaptively. In many settings, Nuts is actually more computationally efficient than the optimal static HMC! When the the Nuts paper appeared on Arxiv, Christian Robert noticed it and had some reactions . In response to Xian’s comments, Matt writes: Christian writes: I wonder about the computing time (and the “una
4 0.12450469 571 andrew gelman stats-2011-02-13-A departmental wiki page?
Introduction: I was recently struggling with the Columbia University philophy department’s webpage (to see who might be interested in this stuff ). The faculty webpage was horrible: it’s just a list of names and links with no information on research interests. So I did some searching on the web and found a wonderful wikipedia page which had exactly what I wanted. Then I checked my own department’s page , and it’s even worse than what they have in philosophy! (We also have this page, which is even worse in that it omits many of our faculty and has a bunch of ridiculously technical links for some of the faculty who are included.) I don’t know about the philosophy department, but the statistics department’s webpage is an overengineered mess, designed from the outset to look pretty rather than to be easily updated. Maybe we could replace it entirely with a wiki? In the meantime, if anybody feels like setting up a wikipedia entry for the research of Columbia’s statistics faculty, that
5 0.12127406 390 andrew gelman stats-2010-11-02-Fragment of statistical autobiography
Introduction: I studied math and physics at MIT. To be more precise, I started in math as default–ever since I was two years old, I’ve thought of myself as a mathematician, and I always did well in math class, so it seemed like a natural fit. But I was concerned. In high school I’d been in the U.S. Mathematical Olympiad training program, and there I’d met kids who were clearly much much better at math than I was. In retrospect, I don’t think I was as bad as I’d thought at the time: there were 24 kids in the program, and I was probably around #20, if that, but I think a lot of the other kids had more practice working on “math olympiad”-type problems. Maybe I was really something like the tenth-best in the group. Tenth-best or twentieth-best, whatever it was, I reached a crisis of confidence around my sophomore or junior year in college. At MIT, I started right off taking advanced math classes, and somewhere along the way I realized I wasn’t seeing the big picture. I was able to do the homework pr
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Introduction: American University in Washington, D.C. has two full-time tenure-line positions available : An ideal candidate will have facility with computation, and can identify specific prospects for on-campus collaboration, possibly interdepartmental. We are particularly interested in candidates who can assist colleagues who need to deal with data sets that are too large, distributed, or heterogeneous to be amenable to traditional methods of analysis. From a mathematician, we also seek a research program with deep roots in mathematics. From a statistician, we seek a familiarity with Bayesian modeling. We are open to researchers who ignore traditional disciplinary boundaries. I like the bits about computation, interdepartmental, and Bayesian modeling. My former colleagues at Berkeley would be spinning in their theorems were they to see this (but, don’t worry, I doubt they read blogs). I really do think that computation is more important than theorem-proving. Both are important (for exam
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Introduction: Two positions open in the statistics group at the NYU education school. If you get the job, you get to work with Jennifer HIll! One position is a postdoctoral fellowship, and the other is a visiting professorship. The latter position requires “the demonstrated ability to develop a nationally recognized research program,” which seems like a lot to ask for a visiting professor. Do they expect the visiting prof to develop a nationally recognized research program and then leave it there at NYU after the visit is over? In any case, Jennifer and her colleagues are doing excellent work, both applied and methodological, and this seems like a great opportunity.
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Introduction: See below for the job announcement. It’s for Teachers College, which is about 2 blocks from the statistics department and 2 blocks from the political science department. So even though I don’t have any official connection with Teachers College (besides occasionally working with them on research projects), I very much would like to have another exciting young applied researcher here, to complement all the people we currently have in stat, poli sci, engineering, etc. In particular, we have zillions of interesting and important social science research projects going on here, and they all need statistics work. A lot of social scientists do statistics, but it’s not so easy to find a statistician who does serious social science research. All this is to say that I hope this job gets some applicants from some people who are serious about applied statistics and the development of new models and methods. Teachers College, Columbia University Department of Human Development APPLIE
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Introduction: Andrew Gelman (Columbia University) and Jennifer Hill (New York University) seek to hire a post-doctoral fellow to work on development of iterative imputation algorithms and diagnostics for missing-data imputation. Activities would include model-development, programming, and data analysis. This project is funded by a grant from the Institute of Education Sciences. Other collaborators on the project include Jingchen Liu and Ben Goodrich. This is a two-year position that would start this summer (2011) or earlier if possible. The ideal candidate will have a statistics or computer science background, will be interested in statistical modeling, serious programming, and applications. He or she should be able to work fluently in R and should already know about hierarchical models and Bayesian inference and computation. Experience or interest in designing GUIs would be a bonus, since we are attempting to improve our GUI for missing data imputation. The successful candidate will beco
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Introduction: American University in Washington, D.C. has two full-time tenure-line positions available : An ideal candidate will have facility with computation, and can identify specific prospects for on-campus collaboration, possibly interdepartmental. We are particularly interested in candidates who can assist colleagues who need to deal with data sets that are too large, distributed, or heterogeneous to be amenable to traditional methods of analysis. From a mathematician, we also seek a research program with deep roots in mathematics. From a statistician, we seek a familiarity with Bayesian modeling. We are open to researchers who ignore traditional disciplinary boundaries. I like the bits about computation, interdepartmental, and Bayesian modeling. My former colleagues at Berkeley would be spinning in their theorems were they to see this (but, don’t worry, I doubt they read blogs). I really do think that computation is more important than theorem-proving. Both are important (for exam
2 0.95948917 1451 andrew gelman stats-2012-08-08-Robert Kosara reviews Ed Tufte’s short course
Introduction: I always wondered what went on there . $380 x 500 people, that’s $190,000! I’ll have to remember to ask for more money next time I’m asked to speak for a commercial organization.
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Introduction: Tiago Fragoso writes: Suppose I fit a two stage regression model Y = a + bx + e a = cw + d + e1 I could fit it all in one step by using MCMC for example (my model is more complicated than that, so I’ll have to do it by MCMC). However, I could fit the first regression only using MCMC because those estimates are hard to obtain and perform the second regression using least squares or a separate MCMC. So there’s an ‘one step’ inference based on doing it all at the same time and a ‘two step’ inference by fitting one and using the estimates on the further steps. What is gained or lost between both? Is anything done in this question? My response: Rather than answering your particular question, I’ll give you my generic answer, which is to simulate fake data from your model, then fit your model both ways and see how the results differ. Repeat the simulation a few thousand times and you can make all the statistical comparisons you like.
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Introduction: Frontiers of Science is a course offered as part of Columbia University’s Core Curriculum. The course is controversial, with some people praising its overview of several areas of science, and others feeling that a more traditional set of introductory science courses would do the job better. Last month, the faculty in charge of the course wrote the following public letter : The United States is in the midst of a debate over the value of a traditional college education. Why enroll in a place like Columbia College when you can obtain an undergraduate degree for $10,000 or learn everything from Massive Open Online Courses? In more parochial terms, what is the value added by approaches such as Columbia’s Core Curriculum? Recently students in our Core Course, Frontiers of Science (FoS), provided a partial answer. The FoS faculty designed a survey to gauge the scientific skills and knowledge of the Class of 2016 both before and after taking FoS. In an assembly held during orientati
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Introduction: From the sister blog, some reasons why the political reaction might be different this time.
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