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ex-quant doing data @shopify. We're hiring data analysts, scientists and statisticians! Screencaster at www.dataorigami.net


2d
comment Survival estimation when death/censoring is probabilistic
@PiotrMigdal have you looked at using customer CLV to measure this? See a good intro in "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model" by Fader et al. An implementation is available at github.com/CamDavidsonPilon/lifetimes
2d
comment Help setting up pymc to solve this problem relating to distribution of colors in M&M's
corrected! Thanks for spotting that.
2d
revised Help setting up pymc to solve this problem relating to distribution of colors in M&M's
added 10 characters in body
2d
answered Help setting up pymc to solve this problem relating to distribution of colors in M&M's
Apr
10
awarded  Popular Question
Mar
24
awarded  Caucus
Mar
24
awarded  Constituent
Mar
10
accepted Inference with only left-censored data
Mar
10
comment Inference with only left-censored data
Neat answer, thanks @whuber. I have a few followup questions if you don't mind. 1. Is there a name for this censorship inference method? 2. What would incorporating observed values look like? You could treat them the same way your treat censorships, but this loses information.
Mar
10
awarded  Nice Answer
Mar
6
accepted Distributed Datasets and MLE
Mar
1
asked Distributed Datasets and MLE
Mar
1
revised Survival Analysis tools in Python
updates to use newer API
Feb
17
reviewed Approve How to interpret the direction of the Harvey-Collier test and Rainbow test for linearity?
Feb
17
reviewed Approve Interpretation of the output from R's penalized linear discriminant analysis, using the penalizedLDA package
Feb
17
reviewed Approve Principal component analysis (PCA) vs. method of principal components for factor analysis (FA)
Feb
11
awarded  Enlightened
Feb
11
awarded  Nice Answer
Feb
5
comment How to simulate a system where “the failure probability per week is 3.5%”?
This also works well because of the forgetfulness property of the exponential: given you have survived this last, the probability you fail this next week is equal to that of last week, i.e. your survival is independent of how long you have survived for. (A consequence of a constant hazard rate). This means that if we start with $N$ machines, there will still always be 3.5% failure each week (on average).
Feb
3
comment PCA objective function: what is the connection between maximizing variance and minimizing error?
That's okay - your answer was the non-mistake version of what I was trying to do.