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19h
answered KS test for Uniformity
22h
revised Is automated machine learning a dream?
added 541 characters in body
23h
revised Is automated machine learning a dream?
typo
23h
comment Checking the assumptions of K-means clustering
With only 75 observations in 22 dimensions, you have a very sparse problem indeed. I'm afraid the curse of dimensionality may bite you, and your clusters may not be very meaningful.
1d
answered Is automated machine learning a dream?
1d
comment Post hoc power analysis in genetic study
Short answer: don't do it.
Jul
3
comment Combining confidence intervals from several regression point estimates
Yes! (Using $t$ distributions with appropriate degrees of freedom, of course, not just uniform draws from p% prediction intervals.)
Jul
3
answered Combining confidence intervals from several regression point estimates
Jul
2
comment Simulation of time series with R
Or use replicate(), as in M <- replicate(100, arima.sim(list(order=c(1,0,2),ar=-0.5,ma=c(0.5,-0.7)),sd=sqrt(0.5),n=60)) (note that then every column of M will contain a time series, not every row).
Jul
1
revised Forecasting with no seasonality
changed title, removed mathematical-statistics tag, added stationarity tag
Jul
1
answered Forecasting with no seasonality
Jun
30
comment Data visualization for missing data
If you have any idea about the intra-day pattern, you can think about including it, but that would be a different plot. What does the vertical position of the line 1/3 of the way from Wed to Thu mean? It can't be that at this point in time, there is a value of (say) 1,200, because the interpolated values already give daily totals. No, the only reason for the lines is to join the dots. And there, I'd go with a simpler rather than a more complex solution. (If three dots form a descending pattern, why should the line have a zero derivative at the central one?) Call me simplicity-obsessed ;-)
Jun
30
comment Data visualization for missing data
I'd go with a straight line for simplicity. Anything "curvy" (like here) suggests that you know that the dynamics differ over the interval you are interpolating. If you do know that, great, and go wild - if not, don't give the impression that you do.
Jun
30
comment Data visualization for missing data
If reviews could have been received but were not (because nobody wanted to review), then interpolating zeros makes sense. Otherwise (e.g., reviews were not received because the system was down), I'd leave holes for however many days there are. I often have exactly this situation in plotting supermarket sales that can be zero for an item, or can be missing if the store is closed (on Sundays in Germany).
Jun
30
revised Data visualization for missing data
added missing-data tag
Jun
30
answered Data visualization for missing data
Jun
24
comment Problems with time series prediction
@karmabob: I just edited the answer to link to a paper on compositional time series forecasting that just appeared - may be interesting.
Jun
24
revised Problems with time series prediction
added 181 characters in body
Jun
20
comment What can we say about population mean from a sample size of 1?
@SimonKuang: yes, you are right, my mistake. Unfortunately, I don't have the time to go through this at this time, but if you do this, please post what you find!
Jun
20
comment What can we say about population mean from a sample size of 1?
@SimonKuang: one conceptual problem is that we can only use the $x\pm 9.68|x|$ interval after we have observed $x$, so this can't enter the prior.