Some additional papers on the point/polygon problem:
Category Archives: statistics
Robust Misunderstanding of Statistics
Interesting paper: http://www.ejwagenmakers.com/inpress/HoekstraEtAlPBR.pdf
Here is the quiz they used:
I’d love to replicate for a few of the target audiences for my work.
Filed under statistics
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:
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Filed under statistics
Marie’s approach to BMI
Another one for the reading list:
Article alert
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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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Filed under global health, statistics
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/
Filed under statistics
Small area estimates by others in PHM
Article alert
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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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Filed under global health, statistics
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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Filed under statistics
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
Filed under MCMC, software engineering, statistics
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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Filed under software engineering, statistics
By no means unhelpful
Good advice from Density Estimation for Statistics and Data Analysis by Bernard. W. Silverman:
Filed under statistics
Healthy Algorithms 