andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2139 knowledge-graph by maker-knowledge-mining
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Introduction: (Click for bigger image.) The above is Aki’s decomposition of the birthdays data (the number of babies born each day in the United States, from 1968 through 1988) using a Gaussian process model, as described in more detail in our book .
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same-blog 1 1.0 2139 andrew gelman stats-2013-12-19-Happy birthday
Introduction: (Click for bigger image.) The above is Aki’s decomposition of the birthdays data (the number of babies born each day in the United States, from 1968 through 1988) using a Gaussian process model, as described in more detail in our book .
2 0.26975209 2067 andrew gelman stats-2013-10-18-EP and ABC
Introduction: Expectation propagation and approximate Bayesian computation. Here are X’s comments on a paper, “Expectation-Propagation for Likelihood-Free Inference,” by Simon Barthelme and Nicolas Chopin. The paper is not new but the topic is still hot. Also there’s this paper by Maurizio Filippone and Mark Girolami on computation for Gaussian process models. I wonder how this connects to GPstuff , which I think is what Aki did to fit the birthdays model: This stuff is where it’s at.
3 0.18358009 1384 andrew gelman stats-2012-06-19-Slick time series decomposition of the birthdays data
Introduction: Aki updates : Here is my plot using the full time series data to make the model. Data analysis could be made in many different ways, but my hammer is Gaussian process, and so I modeled the data with a Gaussian process with six components 1) slowly changing trend 2) 7 day periodical component capturing day of week effect 3) 365.25 day periodical component capturing day of year effect 4) component to take into account the special days and interaction with weekends 5) small time scale correlating noise 6) independent Gaussian noise - Day of the week effect has been increasing in 80′s - Day of year effect has changed only a little during years - 22nd to 31st December is strange time I [Aki] will make the code available this week, but we have to first make new release of our GPstuff toolbox, as I used our development code to do this. I have no idea what’s going on with 29 Feb; I wouldn’t see why births would be less likely on that day. Also, the above graphs are g
4 0.14638229 1357 andrew gelman stats-2012-06-01-Halloween-Valentine’s update
Introduction: A few months ago we reported on a claim that more babies are born on Valentine’s Day and fewer on Halloween. At the time, I wrote that I’d like to see a graph with all 366 days of the year. It would be easy enough to make. That way we could put the Valentine’s and Halloween data in the context of other possible patterns. Joshua Gans sent along the following from an unpublished appendix to his paper. It’s not the graph I was asking for but it does supply additional information beyond those two holidays. Click to enlarge: I don’t know what all those digits are doing (do you really need to know that an estimate is “-70.856″ if its standard error is “10.640″? I’d think that “-71 +/- 10 would be just fine), but I suppose the careful reader can ignore the numbers and simply read the signs and the stars. In any case, it’s good to see more data.
5 0.14475816 1379 andrew gelman stats-2012-06-14-Cool-ass signal processing using Gaussian processes (birthdays again)
Introduction: Aki writes: Here’s my version of the birthday frequency graph . I used Gaussian process with two slowly varying components and periodic component with decay, so that periodic form can change in time. I used Student’s t-distribution as observation model to allow exceptional dates to be outliers. I guess that periodic component due to week effect is still in the data because there is data only from twenty years. Naturally it would be better to model the whole timeseries, but it was easier to just use the cvs by Mulligan. ALl I can say is . . . wow. Bayes wins again. Maybe Aki can supply the R or Matlab code? P.S. And let’s not forget how great the simple and clear time series plots are, compared to various fancy visualizations that people might try. P.P.S. More here .
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same-blog 1 0.94323105 2139 andrew gelman stats-2013-12-19-Happy birthday
Introduction: (Click for bigger image.) The above is Aki’s decomposition of the birthdays data (the number of babies born each day in the United States, from 1968 through 1988) using a Gaussian process model, as described in more detail in our book .
2 0.69674337 1379 andrew gelman stats-2012-06-14-Cool-ass signal processing using Gaussian processes (birthdays again)
Introduction: Aki writes: Here’s my version of the birthday frequency graph . I used Gaussian process with two slowly varying components and periodic component with decay, so that periodic form can change in time. I used Student’s t-distribution as observation model to allow exceptional dates to be outliers. I guess that periodic component due to week effect is still in the data because there is data only from twenty years. Naturally it would be better to model the whole timeseries, but it was easier to just use the cvs by Mulligan. ALl I can say is . . . wow. Bayes wins again. Maybe Aki can supply the R or Matlab code? P.S. And let’s not forget how great the simple and clear time series plots are, compared to various fancy visualizations that people might try. P.P.S. More here .
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Introduction: Really we need the data on babies born 30 years ago, but this is still pretty stunning: Argentina: Matías, #3; Mateo, #13 Australia/New South Wales: Matthew, #21 Australia/Victoria: Matthew, #21 Austria: Matthias, #19 Belgium: Mathis, #9; Matteo, #22; Mathias, #23; Mathéo, #35; Mats, #89; Mathieu, #90; Matthias, #97 Brazil: Matheus, #4 Canada/Alberta: Matthew, #8 Canada/British Columbia: Matthew, #6 Canada/Ontario: Matthew, #2 Canada/Quebec: Mathis, #11; Mathieu, #35; Mathias, #47; Matthew, #76; Mathys, #78; Matis, #84 Canada/Saskatchewan: Matthew, #10 Chile: Matias, #4 Czech Republic: Matej, #7; Matyas, #17; Matous, #25 Denmark: Mathias, #11, Mads, #12 England: Matthew, #24 Finland: Matias, #4 France: Mathis, #3 Georgia: Mate, #8 Germany: Matthis, #87 Hungary: Máté, #2; Matyas, #53 Iceland: Matthias, #32 Ireland: Matthew, #17 Italy: Matteo, #4; Mattia, #7 Lithuania: Matas, #1 Netherlands: Thijs, #13 New Zealand: Matthew, #21 Northern Ireland: Matthew,
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Introduction: We have lots of models for overdispersed count data but we rarely see underdispersed data. But now I know what example I’ll be giving when this next comes up in class. From a book review by Theo Tait: A number of shark species go in for oophagy, or uterine cannibalism. Sand tiger foetuses ‘eat each other in utero, acting out the harshest form of sibling rivalry imaginable’. Only two babies emerge, one from each of the mother shark’s uteruses: the survivors have eaten everything else. ‘A female sand tiger gives birth to a baby that’s already a metre long and an experienced killer,’ explains Demian Chapman, an expert on the subject. That’s what I call underdispersion. E(y)=2, var(y)=0. Take that, M. Poisson!
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Introduction: It seems that every day brings a better system for exploring and sharing data on the Internet. From Iceland comes DataMarket . DataMarket is very good at visualizing individual datasets – with interaction and animation, although the “market” aspect hasn’t yet been developed, and all access is free. Here’s an example of visualizing rankings of countries competing in WorldCup: And here’s a lovely example of visualizing population pyramids : In the future, the visualizations will also include state of the art models for predicting and imputing missing data, and understanding the underlying mechanisms. Other posts: InfoChimps , Future of Data Analysis
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same-blog 1 0.84121078 2139 andrew gelman stats-2013-12-19-Happy birthday
Introduction: (Click for bigger image.) The above is Aki’s decomposition of the birthdays data (the number of babies born each day in the United States, from 1968 through 1988) using a Gaussian process model, as described in more detail in our book .
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Introduction: Aki writes: Here’s my version of the birthday frequency graph . I used Gaussian process with two slowly varying components and periodic component with decay, so that periodic form can change in time. I used Student’s t-distribution as observation model to allow exceptional dates to be outliers. I guess that periodic component due to week effect is still in the data because there is data only from twenty years. Naturally it would be better to model the whole timeseries, but it was easier to just use the cvs by Mulligan. ALl I can say is . . . wow. Bayes wins again. Maybe Aki can supply the R or Matlab code? P.S. And let’s not forget how great the simple and clear time series plots are, compared to various fancy visualizations that people might try. P.P.S. More here .
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Introduction: My Take a Number feature appears in today’s Times. And here are the graphs that I wish they’d had space to include! Original story here .
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Introduction: “A statistical model is usually taken to be summarized by a likelihood, or a likelihood and a prior distribution, but we go an extra step by noting that the parameters of a model are typically batched, and we take this batching as an essential part of the model.”
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Introduction: Frank Hansen writes: Columbus Park is on Chicago’s west side, in the Austin neighborhood. The park is a big green area which includes a golf course. Here is the google satellite view. Here is the nytimes page. Go to Chicago, and zoom over to the census tract 2521, which is just north of the horizontal gray line (Eisenhower Expressway, aka I290) and just east of Oak Park. The park is labeled on the nytimes map. The census data have around 50 dots (they say 50 people per dot) in the park which has no residential buildings. Congressional district is Danny Davis, IL7. Here’s a map of the district. So, how do we explain the map showing ~50 dots worth of people living in the park. What’s up with the algorithm to place the dots? I dunno. I leave this one to you, the readers.
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