Timeline for Solving linear regression with weights and constraints
Current License: CC BY-SA 3.0
22 events
| when toggle format | what | by | license | comment | |
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| S Sep 11, 2014 at 17:57 | history | bounty ended | Datageek | ||
| S Sep 11, 2014 at 17:57 | history | notice removed | Datageek | ||
| Sep 10, 2014 at 18:28 | vote | accept | Datageek | ||
| Sep 10, 2014 at 17:48 | history | tweeted | twitter.com/#!/StackStats/status/509760221125025792 | ||
| Sep 10, 2014 at 17:16 | answer | added | Sycorax♦ | timeline score: 3 | |
| S Sep 10, 2014 at 16:23 | history | bounty started | Datageek | ||
| S Sep 10, 2014 at 16:23 | history | notice added | Datageek | Canonical answer required | |
| Sep 10, 2014 at 16:22 | history | edited | Datageek | CC BY-SA 3.0 |
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| Sep 8, 2014 at 19:18 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Sep 8, 2014 at 18:29 | vote | accept | Datageek | ||
| Sep 10, 2014 at 18:28 | |||||
| Sep 8, 2014 at 18:14 | comment | added | Sycorax♦ | @whuber I think I've finally cracked it, but since this is the first time I've ever attempted to solve a problem like this, I'd appreciate your expert feedback. Thanks! | |
| Sep 8, 2014 at 16:23 | comment | added | whuber♦ | There is no essential mathematical difference between bounding the slope and bounding the sum of coefficients: both are bounds on linear combinations of the coefficients. The solutions offered to those questions apply with very little change to your slightly more general formulation, thereby immediately giving you access to a variety of approaches to choose from. However, I have not voted to close your question, because although it does appear to be answered elsewhere, evidently it does take a little mathematical manipulation to see that those answers can apply. | |
| Sep 8, 2014 at 16:10 | comment | added | Datageek | @whuber Thanks, I was looking at other threads as well. The four different ways address a problem of a slope within the borders, not the sum of coefficients. The first link was helpful but I am still struggling to wrap my head around solve.QP or mgcv. I was hoping my question is generic enough to be useful for others and also sufficiently different from existing solutions in other threads. | |
| Sep 8, 2014 at 16:03 | comment | added | Datageek | @user777 I liked your solution as it was addressing this specific problem. If possible can you add it back? If anything it should be informative to someone else. | |
| Sep 8, 2014 at 16:02 | comment | added | whuber♦ |
@user777 I am sorry about that: I was thinking that if these threads were close enough, we could merge your answer with the ones there. I liked your explicit demonstration of rstan, which has no parallel in the answers within the other threads.
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| Sep 8, 2014 at 15:54 | comment | added | whuber♦ | Very nearly the same question is asked--and answered in four different ways--at stats.stackexchange.com/questions/61733. It differs only in explicitly addressing the two dimensional case. | |
| Sep 8, 2014 at 15:49 | history | reopened | whuber♦ | ||
| Sep 8, 2014 at 15:48 | history | closed | whuber♦ | Duplicate of References for weighted linear regression with linear constraints on the coefficients? | |
| Sep 8, 2014 at 15:48 | comment | added | whuber♦ | Note that the weights are not a complication at all, because they can be absorbed in the values of $b$ and $A$, leading to an ordinary least squares problem with a single linear constraint. That means you problem is solved as described at stats.stackexchange.com/questions/24193 . | |
| Sep 8, 2014 at 15:20 | answer | added | Sycorax♦ | timeline score: 6 | |
| Sep 8, 2014 at 13:00 | history | edited | Datageek | CC BY-SA 3.0 |
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| Sep 8, 2014 at 12:48 | history | asked | Datageek | CC BY-SA 3.0 |