Timeline for Inequality-constrained linear-regression, what is the covariance of the estimator?
Current License: CC BY-SA 2.5
8 events
| when toggle format | what | by | license | comment | |
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| Oct 21, 2010 at 10:19 | comment | added | simon | Yes: the posterior distribution is intractable (that is, it doesn't have a nice analytic density function), so a random number generator (in this case, a Gibbs sampler) is used to obtain a sample, from which we can compute the quantities of interest (mean, credible intervals, etc.) | |
| Oct 20, 2010 at 21:58 | vote | accept | Tony Bruguier | ||
| Oct 20, 2010 at 21:58 | vote | accept | Tony Bruguier | ||
| Oct 20, 2010 at 21:58 | |||||
| Oct 20, 2010 at 21:58 | vote | accept | Tony Bruguier | ||
| Oct 20, 2010 at 21:58 | |||||
| Oct 19, 2010 at 19:01 | comment | added | Tony Bruguier | Thanks for the reference. I have been reading it, but I am not done yet. Just so that I make sure I understand correctly, the approach still uses a random number generator, right? It's just a smart way to go at it. | |
| Oct 14, 2010 at 15:13 | comment | added | simon | I haven't read it, but this paper gives a method of computation for a Bayesian approach: stat.duke.edu/~scs/Courses/Stat376/Papers/Constraints/… | |
| Oct 14, 2010 at 14:27 | comment | added | Tony Bruguier | Brian and Simon, Thanks for your answers. If the constraints are very narrow, then the vector $\hat{x}$ will most likely behave like a binary variable. If the constraints are very loose, then it $\hat{x}$ will behave like a normal variable. Sorry, I don't know how you would use Bayesian approach to solve this problem. Could you enlighten me? Also, please note that I am not interested in the pdf of $\hat{x}$, just its covariance matrix. I understand that a canned answer may or may not exist. The paper I reference seems to indicate that in 1976, there was no such answer. | |
| Oct 14, 2010 at 11:52 | history | answered | simon | CC BY-SA 2.5 |