14h
comment How to Assess the Fit of Thousands of Distributions?
The most practical, and really the best answer, for me is the first comment by @NickCox, but since he didn't put his comment as an answer, and he approves of this answer, I am accepting it as the correct answer.
19h
accepted How to Assess the Fit of Thousands of Distributions?
Mar
24
comment How to Assess the Fit of Thousands of Distributions?
You can't really use KS here, because the parameters for the NULL distribution need to pre-specified in KS. You can generate the NULL distribution using Monte-Carlo, but that's something that I am trying to avoid.
Mar
24
comment How to Assess the Fit of Thousands of Distributions?
@ChrisC Isn't $\chi^2$ for discrete random variables? Here we have a continuous distribution.
Mar
24
comment How to Assess the Fit of Thousands of Distributions?
Ideally, one per subject, but I can live with a single measure.
Mar
24
revised How to Assess the Fit of Thousands of Distributions?
Changed 'test' to 'assess'
Mar
24
comment How to Assess the Fit of Thousands of Distributions?
@Glen_b, Yes, I mean assess. Thanks for pointing it out.
Mar
24
asked How to Assess the Fit of Thousands of Distributions?
Dec
17
awarded Notable Question
Nov
27
comment Can mean plus one standard deviation exceed maximum value?
Maybe she's just thinking of a Normal distribution?
Oct
6
awarded Tumbleweed
Sep
29
asked Detecting false positives of a classification algorithm
Sep
3
asked match <U+FEFF> in Java
Aug
21
comment Resources for Computational Algorithms
Thanks. I'm aware of the progress that has been made in data mining and machine algorithms. For those, there is in fact an explosion of resources. What I meant was computational issues in statistics. Something as simple as calculating variance is a nontrivial problem (see for instance Welford's algorithm.)
Aug
21
comment Resources for Computational Algorithms
Originally, I was looking for an online algorithm to calculate a moving variance.
Aug
21
comment Validating a logistic regression for a specific $x$
Interesting thoughts, but there is a difference between the two. By law of large numbers, we know that $\hat{p}_{1|x_0}$ converges to the true probability. However, for logistic regression, there is no such result.
Aug
21
asked Resources for Computational Algorithms
Aug
11
awarded Yearling
Aug
10
awarded Supporter
Jul
2
awarded Yearling
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