meta user
network profile
| bio | website | |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 1 year, 10 months |
| seen | 1 hour ago | |
| stats | profile views | 32 |
Trying to learn statistics, haphazardly.
|
Jul 27 |
awarded | Nice Answer |
|
Jul 27 |
comment |
Beta binomial Bayesian updating over many iterations (I presume a sort of ad hoc engineering approach here) Then I would suggest dividing by an $N$ every week (or day, hour, etc..). I.e., (1) above. This will discount observations last week by $N$, observations from the week before that by $N^2$ and so on. What you are in effect doing is a weighted average where you give more weight to more recent observations. |
|
Jul 27 |
awarded | Scholar |
|
Jul 27 |
accepted | What is the expected value of the sample variance under a linear regression with omitted variables of an AR(2) process? |
|
Jul 27 |
answered | How to form a meaningful statistical indicator to reflect user interaction with a website |
|
Jul 27 |
awarded | Teacher |
|
Jul 27 |
answered | Why use Monte Carlo method instead of a simple grid? |
|
Jul 27 |
revised |
Beta binomial Bayesian updating over many iterations deleted 12 characters in body |
|
Jul 27 |
revised |
Beta binomial Bayesian updating over many iterations added 314 characters in body |
|
Jul 27 |
answered | Beta binomial Bayesian updating over many iterations |
|
Jul 26 |
comment |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ I agree. Do you have any good references for that (aside from the original paper). I found Stein's original paper overly computational and was hoping that someone would have developed a different method in the last fifty years. |
|
Jul 26 |
revised |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ edited title |
|
Jul 26 |
revised |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ deleted 1 characters in body |
|
Jul 26 |
revised |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ added 200 characters in body |
|
Jul 26 |
comment |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ Sorry, I should have explicitly mentioned that Stein proved that for $N=2$, the MLE is admissible! See projecteuclid.org/… |
|
Jul 26 |
comment |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ @mpiktas 1) Yes, the setup is purely theoretical. But still really important (for example, we can let the variables be means of i.i.ds). 2) Yes, although I don't know what you mean with required properties. 3) See above. 4) True, but the expected value of $1/S$ is not defined for $N=1$. (the inverse chi square distribution doesn't have a mean for $N=1$). |
|
Jul 26 |
revised |
Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ added 461 characters in body |
|
Jul 26 |
asked | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ |
|
Jul 25 |
awarded | Supporter |
|
Jul 20 |
comment |
What is the expected value of the sample variance under a linear regression with omitted variables of an AR(2) process? When you are saying $RSS_p = RSS_{p-1}(1-\phi^2_{pp})$, do you mean $\mathbb{E}(RSS_p) = \mathbb{E}(RSS_{p-1}(1-\phi^2_{pp}))$? It seems to me that the only sensible thing we can talk about is the expected value of the $RSS$. |
