Impact
~71k
people reached
- 0 posts edited
- 0 helpful flags
- 1,242 votes cast
Oct
15 |
comment |
Estimating direction from a distribution on a circle
I think, due to the symmetry, it essential does act in a linear way. I'm pretty sure I can describe very accurately how well your estimator will work. I'll post this as an(other) answer a bit later. |
Oct
15 |
comment |
Estimating direction from a distribution on a circle
I will Will, I will. Just give me a little time. |
Oct
15 |
comment |
Estimating direction from a distribution on a circle
Actually, I lie. In this case, I think this is exactly what you want to do! I recommend this answer be marked as correct. My answers can just be considered as advertising for the important and interesting the field of circular statistics. |
Oct
15 |
comment |
Estimating direction from a distribution on a circle
That's a good answer Niels, and I believe it will work quite well in this case. One trouble I see is that by squaring you have multiplied the 'noise' (if you like) by 2. So, I think that this estimator will be consistent, but not efficient. |
Oct
14 |
comment |
Estimating direction from a distribution on a circle
@Andrei: If you have further questions, feel free to send me an email. |
Oct
14 |
comment |
Estimating direction from a distribution on a circle
Hi Will, I use metapost. The actually data for the pdf (it's a weighted sum of von Mises distributions by the way) comes from a java library (unfortunately) and then the plotting gets done by metapost. At some point I plan on releasing all of the code I have for simulations and plotting under the CRAPL matt.might.net/articles/crapl. However, at the moment I feel the code is even too crap for the CRAPL :( |
Oct
14 |
comment |
A stochastic process that is 1st and 2nd order (strictly) stationary, but not 3rd order stationary
@Pablo: Thanks very much for this! This is a definitely a neat and simple example. |
Oct
14 |
accepted | A stochastic process that is 1st and 2nd order (strictly) stationary, but not 3rd order stationary |
Oct
14 |
answered | Estimating direction from a distribution on a circle |
Oct
14 |
answered | Estimating direction from a distribution on a circle |
Oct
14 |
asked | A stochastic process that is 1st and 2nd order (strictly) stationary, but not 3rd order stationary |
Oct
10 |
comment |
Is the operator norm always attained on a $\{0,1\}$-vector?
Nice answer! Is it true that your vector $v$ is an example of what is sometimes called a `badly approximable vector'? |
Oct
3 |
comment |
You pass X people and Y people pass you: how relatively fast are you?
I actually just got back from a run when I read this question. I almost want to go for another run to experiment :) |
Oct
3 |
comment |
You pass X people and Y people pass you: how relatively fast are you?
+1 I really like this question! |
Sep
28 |
comment |
The difference between Principal Components Analysis (PCA) and Factor Analysis (FA)
You will probably get a better answer to this on stats.stackexchange.com/questions |
Sep
19 |
comment |
estimate the error term in CLT
@mr.gondolier: Is $f(x) = x^4$ allowed? You state in the question that $f(x)$ is bounded. |
Sep
5 |
comment |
Computing equivalent vector of random variables from covarience matrix
@Didier: See my comment on Darsh's answer. |
Sep
4 |
comment |
Computing equivalent vector of random variables from covarience matrix
It depends on what you are doing. Say you want uniformly distributed random variables with a particular correlation structure. You can't just generate uncorrelated uniform random variables and apply the Cholesky decomposition because a sum of uniform random variables is no longer uniform. It will have the correct correlation (as you show) but the marginal distributions will not be uniform. |
Sep
4 |
comment |
get standard error from correlation coefficient?
You will probably have more luck with this question on stats.stackexchange.com. |
Sep
4 |
answered | Computing equivalent vector of random variables from covarience matrix |