andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1829 knowledge-graph by maker-knowledge-mining
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Introduction: Sam Behseta writes: There is a report by Martin Tingley and Peter Huybers in Nature on the unprecedented high temperatures at northern latitudes (Russia, Greenland, etc). What is more interesting is the authors are have used a straightforward hierarchical Bayes model, and for the first time (as far as I can remember) the results are reported with a probability attached to them (P>0.99), as opposed to the usual p-value<0.01 business. This might be a sign that editors of big time science journals are welcoming Bayesian approaches. I agree. This is a good sign for statistical communication. Here are the key sentences from the abstract: Here, using a hierarchical Bayesian analysis of instrumental, tree-ring, ice-core and lake-sediment records, we show that the magnitude and frequency of recent warm temperature extremes at high northern latitudes are unprecedented in the past 600 years. The summers of 2005, 2007, 2010 and 2011 were warmer than those of all prior years back to 1
sentIndex sentText sentNum sentScore
1 Sam Behseta writes: There is a report by Martin Tingley and Peter Huybers in Nature on the unprecedented high temperatures at northern latitudes (Russia, Greenland, etc). [sent-1, score-0.942]
2 What is more interesting is the authors are have used a straightforward hierarchical Bayes model, and for the first time (as far as I can remember) the results are reported with a probability attached to them (P>0. [sent-2, score-0.427]
3 This might be a sign that editors of big time science journals are welcoming Bayesian approaches. [sent-5, score-0.356]
4 This is a good sign for statistical communication. [sent-7, score-0.144]
5 The summers of 2005, 2007, 2010 and 2011 were warmer than those of all prior years back to 1400 (probability P > 0. [sent-9, score-0.217]
6 The summer of 2010 was the warmest in the previous 600 years in western Russia (P > 0. [sent-11, score-0.563]
7 99) and probably the warmest in western Greenland and the Canadian Arctic as well (P > 0. [sent-12, score-0.479]
8 These and other recent extremes greatly exceed those expected from a stationary climate, but can be understood as resulting from constant space–time variability about an increased mean temperature. [sent-14, score-1.02]
9 As with classical p-values, these probability statements depend on an assumed model, but I agree with Sam that the expression of direct probabilities is a huge step forward from traditional practice. [sent-15, score-0.374]
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same-blog 1 0.99999988 1829 andrew gelman stats-2013-04-28-Plain old everyday Bayesianism!
Introduction: Sam Behseta writes: There is a report by Martin Tingley and Peter Huybers in Nature on the unprecedented high temperatures at northern latitudes (Russia, Greenland, etc). What is more interesting is the authors are have used a straightforward hierarchical Bayes model, and for the first time (as far as I can remember) the results are reported with a probability attached to them (P>0.99), as opposed to the usual p-value<0.01 business. This might be a sign that editors of big time science journals are welcoming Bayesian approaches. I agree. This is a good sign for statistical communication. Here are the key sentences from the abstract: Here, using a hierarchical Bayesian analysis of instrumental, tree-ring, ice-core and lake-sediment records, we show that the magnitude and frequency of recent warm temperature extremes at high northern latitudes are unprecedented in the past 600 years. The summers of 2005, 2007, 2010 and 2011 were warmer than those of all prior years back to 1
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Introduction: From my new article in the journal Epidemiology: Sander Greenland and Charles Poole accept that P values are here to stay but recognize that some of their most common interpretations have problems. The casual view of the P value as posterior probability of the truth of the null hypothesis is false and not even close to valid under any reasonable model, yet this misunderstanding persists even in high-stakes settings (as discussed, for example, by Greenland in 2011). The formal view of the P value as a probability conditional on the null is mathematically correct but typically irrelevant to research goals (hence, the popularity of alternative—if wrong—interpretations). A Bayesian interpretation based on a spike-and-slab model makes little sense in applied contexts in epidemiology, political science, and other fields in which true effects are typically nonzero and bounded (thus violating both the “spike” and the “slab” parts of the model). I find Greenland and Poole’s perspective t
3 0.1437564 180 andrew gelman stats-2010-08-03-Climate Change News
Introduction: I. State of the Climate report The National Oceanic and Atmospheric Administration recently released their “State of the Climate Report” for 2009 . The report has chapters discussing global climate (temperatures, water vapor, cloudiness, alpine glaciers,…); oceans (ocean heat content, sea level, sea surface temperatures, etc.); the arctic (sea ice extent, permafrost, vegetation, and so on); Antarctica (weather observations, sea ice extent,…), and regional climates. NOAA also provides a nice page that lets you display any of 11 relevant time-series datasets (land-surface air temperature, sea level, ocean heat content, September arctic sea-ice extent, sea-surface temperature, northern hemisphere snow cover, specific humidity, glacier mass balance, marine air temperature, tropospheric temperature, and stratospheric temperature). Each of the plots overlays data from several databases (not necessarily indepenedent of each other), and you can select which ones to include or leave
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Introduction: The National Climatic Data Center has tentatively announced that 2010 is, get this, “tied” for warmest on record. Presumably they mean it’s tied to the precision that they quote (1.12 F above the 20th-century average). The uncertainty in the measurements, as well as some fuzziness about exactly what is being measured (how much of the atmosphere, and the oceans) makes these global-average things really suspect. For instance, if there’s more oceanic turnover one year, that can warm the deep ocean but cool the shallow ocean and atmosphere, so even though the heat content of the atmosphere-ocean system goes up, some of these “global-average” estimates can go down. The reverse can happen too. And of course there are various sources of natural variability that are not, these days, what most people are most interested in. So everybody who knows about the climate professes to hate the emphasis on climate records. And yet, they’re irresistible. I’m sure we’ll see the usual clamor of som
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Introduction: Americans (including me) don’t know much about other countries. Jeff Lax sent me to this blog post by Myrddin pointing out that Belgium has a higher murder rate than the rest of Western Europe. I have no particular take on this, but it’s a good reminder that other countries differ from each other. Here in the U.S., we tend to think all western European countries are the same, all eastern European countries are the same, etc. In reality, Sweden is not Finland . P.S. According to the Wiki , Greenland is one tough town. I guess there’s nothing much to do out there but watch satellite TV, chew the blubber, and kill people.
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Introduction: Sam Behseta writes: There is a report by Martin Tingley and Peter Huybers in Nature on the unprecedented high temperatures at northern latitudes (Russia, Greenland, etc). What is more interesting is the authors are have used a straightforward hierarchical Bayes model, and for the first time (as far as I can remember) the results are reported with a probability attached to them (P>0.99), as opposed to the usual p-value<0.01 business. This might be a sign that editors of big time science journals are welcoming Bayesian approaches. I agree. This is a good sign for statistical communication. Here are the key sentences from the abstract: Here, using a hierarchical Bayesian analysis of instrumental, tree-ring, ice-core and lake-sediment records, we show that the magnitude and frequency of recent warm temperature extremes at high northern latitudes are unprecedented in the past 600 years. The summers of 2005, 2007, 2010 and 2011 were warmer than those of all prior years back to 1
Introduction: I hate to keep bumping our scheduled posts but this is just too important and too exciting to wait. So it’s time to jump the queue. The news is a paper from Michael Betancourt that presents a super-cool new way to compute normalizing constants: A common strategy for inference in complex models is the relaxation of a simple model into the more complex target model, for example the prior into the posterior in Bayesian inference. Existing approaches that attempt to generate such transformations, however, are sensitive to the pathologies of complex distributions and can be difficult to implement in practice. Leveraging the geometry of thermodynamic processes I introduce a principled and robust approach to deforming measures that presents a powerful new tool for inference. The idea is to generalize Hamiltonian Monte Carlo so that it moves through a family of distributions (that is, it transitions through an “inverse temperature” variable called beta that indexes the family) a
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Introduction: I. State of the Climate report The National Oceanic and Atmospheric Administration recently released their “State of the Climate Report” for 2009 . The report has chapters discussing global climate (temperatures, water vapor, cloudiness, alpine glaciers,…); oceans (ocean heat content, sea level, sea surface temperatures, etc.); the arctic (sea ice extent, permafrost, vegetation, and so on); Antarctica (weather observations, sea ice extent,…), and regional climates. NOAA also provides a nice page that lets you display any of 11 relevant time-series datasets (land-surface air temperature, sea level, ocean heat content, September arctic sea-ice extent, sea-surface temperature, northern hemisphere snow cover, specific humidity, glacier mass balance, marine air temperature, tropospheric temperature, and stratospheric temperature). Each of the plots overlays data from several databases (not necessarily indepenedent of each other), and you can select which ones to include or leave
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Introduction: After writing yesterday’s post , I was going through Solomon Hsiang’s blog and found a post pointing to three studies from researchers at business schools: Severe Weather and Automobile Assembly Productivity Gérard P. Cachon, Santiago Gallino and Marcelo Olivares Abstract: It is expected that climate change could lead to an increased frequency of severe weather. In turn, severe weather intuitively should hamper the productivity of work that occurs outside. But what is the effect of rain, snow, fog, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, one additional day of high wind advisory by the National Weather Service (i.e., maximum winds generally in excess of 44 miles per hour) reduces production by 26%, which is comparable in order of magnitude t
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Introduction: Dave Judkins writes: I would love to see a blog entry on this article , Bayesian Model Selection in High-Dimensional Settings, by Valen Johnson and David Rossell. The simulation results are very encouraging although the choice of colors for some of the graphics is unfortunate. Unless I am colorblind in some way that I am unaware of, they have two thin charcoal lines that are indistinguishable. When Dave Judkins puts in a request, I’ll respond. Also, I’m always happy to see a new Val Johnson paper. Val and I are contemporaries—he and I got our PhD’s at around the same time, with both of us working on Bayesian image reconstruction, then in the early 1990s Val was part of the legendary group at Duke’s Institute of Statistics and Decision Sciences—a veritable ’27 Yankees featuring Mike West, Merlise Clyde, Michael Lavine, Dave Higdon, Peter Mueller, Val, and a bunch of others. I always thought it was too bad they all had to go their separate ways. Val also wrote two classic p
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Introduction: Alexander at GiveWell writes : The Disease Control Priorities in Developing Countries (DCP2), a major report funded by the Gates Foundation . . . provides an estimate of $3.41 per disability-adjusted life-year (DALY) for the cost-effectiveness of soil-transmitted-helminth (STH) treatment, implying that STH treatment is one of the most cost-effective interventions for global health. In investigating this figure, we have corresponded, over a period of months, with six scholars who had been directly or indirectly involved in the production of the estimate. Eventually, we were able to obtain the spreadsheet that was used to generate the $3.41/DALY estimate. That spreadsheet contains five separate errors that, when corrected, shift the estimated cost effectiveness of deworming from $3.41 to $326.43. [I think they mean to say $300 -- ed.] We came to this conclusion a year after learning that the DCP2’s published cost-effectiveness estimate for schistosomiasis treatment – another kind of
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Introduction: I received the following (unsolicited) email: Hello, *** LLC, a ***-based market research company, has a financial client who is interested in speaking with a statistician who has done research in the field of Alzheimer’s Disease and preferably familiar with the SOLA and BAPI trials. We offer an honorarium of $200 for a 30 minute telephone interview. Please advise us if you have an employment or consulting agreement with any organization or operate professionally pursuant to an organization’s code of conduct or employee manual that may control activities by you outside of your regular present and former employment, such as participating in this consulting project for MedPanel. If there are such contracts or other documents that do apply to you, please forward MedPanel a copy of each such document asap as we are obligated to review such documents to determine if you are permitted to participate as a consultant for MedPanel on a project with this particular client. If you are
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Introduction: Troels Ring writes: I have measured total phosphorus, TP, on a number of dialysis patients, and also measured conventional phosphate, Pi. Now P is exchanged with the environment as Pi, so in principle a correlation between TP and Pi could perhaps be expected. I’m really most interested in the fraction of TP which is not Pi, that is TP-Pi. I would also expect that to be positively correlated with Pi. However, looking at the data using a mixed model an insignificant negative correlation is obtained. Then I thought, that since TP-Pi is bound to be small if Pi is large a negative correlation is almost dictated by the math even if the biology would have it otherwise in so far as the the TP-Pi, likely organic P, must someday have been Pi. Hence I thought about correcting the slight negative correlation between TP-Pi and Pi for the expected large negative correlation due to the math – to eventually recover what I came from: a positive correlation. People seems to agree that this thinki
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