Jul
11
revised Are there mathematical reasons for convolution in neural networks beyond expediency?
added 31 characters in body
Jun
26
awarded  Nice Answer
Jun
15
answered How to handle timeseries extremes (sigma > 20) in deep learning?
Jun
15
awarded  Benefactor
Jun
15
accepted Using indistinguishable subjects as predictors/random effects
Jun
14
reviewed Leave Open How to apply VAR model for a I(1) and other I(0) variable? Objective is to forecast
Jun
13
answered Using indistinguishable subjects as predictors/random effects
Jun
10
revised Using indistinguishable subjects as predictors/random effects
edited title
Jun
7
awarded  Promoter
Jun
4
comment Why do we take the square root of variance to create standard deviation?
We have that already, in the form of the mean (or median) absolute deviation, L1 norms, and the like. However, the major advantage of the traditional approach is that, unlike absolute values, it's differentiable, which allows you to analytically minimize and maximize things.
Jun
3
awarded  Curious
Jun
3
revised Using indistinguishable subjects as predictors/random effects
Added more about indistinguishability.
Jun
3
comment Using indistinguishable subjects as predictors/random effects
Right, but what about a pair of examples where one's id_i equals the other's id_j? In other words, <Alice, Bob> is correlated with <Bob, Charlie>, even though neither the first ids nor the second match.
Jun
3
comment Using indistinguishable subjects as predictors/random effects
It's certainly possible that I'm overthinking this and your solution is perfect, but ideally I'd like a slightly more rigorous argument than "well, it runs". (Not that I don't appreciate the effort!)
Jun
3
comment Using indistinguishable subjects as predictors/random effects
This will definitely run, and I'm reasonably convinced it's the right solution for distinguishable subjects (e.g., $i$ indexes over buyers, $j$ over sellers). I'm worried that it's not quite right for indistinguishable subjects though: the observations where $i=1,j=2$, for example, are not independent of $i=2,j=3$, since both involve subject 2. In other words, your model has two effects of subjects (id_i, id_j), but I'm shooting for something where there's just one (I think).
Jun
1
asked Using indistinguishable subjects as predictors/random effects
Apr
30
answered Are there mathematical reasons for convolution in neural networks beyond expediency?
Apr
30
answered Why is there a change in the number of degrees of freedom when the following modification is made?
Apr
20
awarded  Enlightened
Apr
20
awarded  Nice Answer