7,801 reputation
1937
bio website thinkinator.com
location Washington DC, United States
age
visits member for 4 years, 3 months
seen 8 mins ago

Data Scientist at ERI, an incredible analytics company where we do everything from fraud detection and visualization, to training and and text mining. (Photo by Peter B.)


2d
comment Forecasting in Stata
@Nox: I think you want mse not stdp. Does that work?
2d
comment Should the fumble rate of NFL teams be a normal distribution?
@JohnBabson: Plays are not called at random. A team that emphasizes passing will tend to throw the ball away when plays fall apart, which means a fumble is not possible. We would also expect fewer fumbles from teams that run in such a way that unexpected hits to their ball carriers are rarer. It doesn't "equal out" over teams and seasons; it's correlated to playing style, the coach's strategy (and what talent they have available to pursue that strategy), plus how much the coach hates turnovers. No coach likes them, but some coaches will bench you for the game, and some for the season.
Jan
27
comment Forecasting in Stata
Great to hear! I'm an R guy myself, though I do like Stata a lot. It's a bit less flexible (or perhaps it requires more rigorous specifications) than R. I'm surprised that your R colleague got a dynamic forecast from a non-arima. My guess is his input data was a ts with a bunch of NA's at the end or something.
Jan
27
revised Forecasting in Stata
added 191 characters in body
Jan
27
revised Forecasting in Stata
Corrected "STATA" to "Stata".
Jan
27
answered Forecasting in Stata
Jan
27
comment Should the fumble rate of NFL teams be a normal distribution?
Also, are you referring to the distribution of an individual teams fumbles per game per season, or of all teams per game in a particular season, or of all teams per game over time?
Jan
18
revised How to use DLM with Kalman filtering for forecasting
Deleted an improper "don't" which reversed the meaning.
Jan
7
awarded  Nice Answer
Jan
2
answered Why not validate on the entire training set?
Dec
6
awarded  Notable Question
Dec
3
answered What is a good Gini decrease cutoff for feature inclusion based upon random forests?
Dec
2
comment Questions revolving GMM & EM
Supervised v Unsupervised has to do with whether the algorithm uses training data that is labeled or not, respectively. You're correct that specifying or learning K has a supervised or unsupervised flavor to it, but that application of the terms would be non-standard and misleading.
Dec
2
comment clustering vs fitting with a distribution
Have you looked at stats.stackexchange.com/questions/69424/…
Dec
1
comment Taking only a single data set from a multiple imputation?
EdM: Very good thought, I'll look into it. Not sure how easy it will be to tap into the output of downstream code, combine things, and re-inject for even further-downstream. I'd still be interested in statistical answers as well.
Dec
1
asked Taking only a single data set from a multiple imputation?
Nov
28
comment Creating a “certainty score” from the votes in random forests?
From R's randomForest package help: $type$: one of "response", "prob", or "votes", indicating the type of output: predicted values, matrix of class probabilities, or matrix of vote counts.
Nov
26
comment Marginalising over standard deviation of normal to get the posterior on mean
Sounds like you'd need to create your own step method in pymc, or perhaps you could use a Beta distribution instead of Normal. I'm not an expert on this, though.
Nov
22
comment How can I analyze my incoming email?
Word clouds are colorful, entertaining, and a whole host of things, but they're not useful for any kind of analysis. Perhaps if you were writing poetry or a song based on the words, but not for analysis.
Nov
21
comment How can I analyze my incoming email?
@Superbest: 1) Wolfram's exploratory approach is useful, even if specific measurements are not applicable in your case. 2) Even if the timing of incoming emails is not important for announcements from a particular source, that doesn't mean it's not important in other cases. 3) Word clouds are a waste of time.