Category Archives: statistics

Point/Polygon Pubs

Some additional papers on the point/polygon problem:

Click to access 1505.06891v1.pdf


Click to access 1608.03769.pdf

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Robust Misunderstanding of Statistics

Interesting paper: http://www.ejwagenmakers.com/inpress/HoekstraEtAlPBR.pdf

Here is the quiz they used:
ci_survey
I’d love to replicate for a few of the target audiences for my work.

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Reading up on Spatial Big Data

So much to read:

Click to access CressieMassiveData.pdf


Click to access cressie_FRK.pdf

Click to access 1512.09327v1.pdf

My brother wrote a tutorial, feedback welcome:

https://flaxter.shinyapps.io/zoowriteup/zoowriteup.Rmd

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Marie’s approach to BMI

Another one for the reading list:

Article alert
________________________________________
The following new articles have just been published in Population Health Metrics

Research
A novel method for estimating distributions of body mass index
Ng M, Liu P, Thomson B, Murray C
Population Health Metrics 2016, 14 :6 (12 March 2016)

http://pophealthmetrics.biomedcentral.com/articles/10.1186/s12963-016-0076-2

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Delta Time in Python: Simple calendar times with Pandas

Here is something that Google did not help with as quickly as I would have expected: how do I convert start and stop times into the time between events in seconds (or minutes)?

Or for the busy searcher “how do I convert Pandas Timedelta to seconds”?

The classy answer is:

start_time = df.interviewstarttime.map(pd.Timestamp)
end_time = df.interviewendtime.map(pd.Timestamp)

((end_time-start_time) / pd.Timedelta(minutes=1)).describe()

I found it hidden away here: http://www.datasciencebytes.com/bytes/2015/05/16/pandas-timedelta-histograms-unit-conversion-and-overflow-danger/

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Small area estimates by others in PHM

Article alert
________________________________________
The following new article has just been published in Population Health Metrics

Research
Small area synthetic estimates of smoking prevalence during pregnancy in England
Szatkowski L, Fahy S, Coleman T, Taylor J, Twigg L, Moon G, Leonardi-Bee J
Population Health Metrics 2015, 13 :34 (9 December 2015)

http://www.pophealthmetrics.com/content/13/1/34

________________________________________

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Laplace approximation in PyMC3, revisited

I found an even better example of the value of Laplace approximation, and its just a small tweak to the example I did a few weeks ago: http://nbviewer.ipython.org/gist/aflaxman/6d0a9ff2441348f3a130

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MCMC in Python: Gaussian mixture model in PyMC3

PyMC3 is really coming along. I tried it out on a Gaussian mixture model that was the subject of some discussion on GitHub: https://github.com/pymc-devs/pymc3/issues/443#issuecomment-109813012 http://nbviewer.ipython.org/gist/aflaxman/64f22d07256f67396d3a

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Laplace approximation in Python: another cool trick with PyMC3

I admit that I’ve been skeptical of the complete rewrite of PyMC that underlies version 3. It seemed to me motivated by an interest in using unproven new step methods that require knowing the derivative of the posterior distribution. But, it is really coming together, and regardless of whether or not the Hamiltonian Monte Carlo stuff pays off, there are some cool tricks you can do when you can get derivatives without a hassle.

Exhibit 1: A Laplace approximation approach to fitting mixed effect models (as described in http://www.seanet.com/~bradbell/tmb.htm)

http://nbviewer.ipython.org/gist/aflaxman/9dab52248d159e02b2ae

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By no means unhelpful

Good advice from Density Estimation for Statistics and Data Analysis by Bernard. W. Silverman:

https://books.google.com/books?id=e-xsrjsL7WkC&lpg=PP1&dq=density%20estimation%20silverman&pg=PA45#v=onepage&q=statistician%20scientist&f=false

Capture

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