Timeline for When would least squares be a bad idea?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Jan 30, 2015 at 11:15 | history | edited | Peter Flom | CC BY-SA 3.0 |
added 90 characters in body
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| Jan 20, 2014 at 23:32 | comment | added | jpmuc | Great point,+1. Just consider the expression $(x-a)^{2}+(x-b)^{2}$ and calculate its minimum. It is the midpoint between a and b. This is contrast with the $L_{1}$ error function, a.k.a. robust regression | |
| Jan 20, 2014 at 23:30 | comment | added | Peter Flom | Yes, I meant the mean of Y. That is what OLS regression does. | |
| Jan 20, 2014 at 23:08 | comment | added | Manuel | Or did you meant, the mean of $Y$ ? | |
| Jan 20, 2014 at 23:05 | comment | added | Manuel | what do you mean with not wanting to estimate the mean? I am considering $\beta$ as a fixed parameter in a frequentist approach, if that what you are talking about. | |
| Jan 20, 2014 at 22:58 | history | answered | Peter Flom | CC BY-SA 3.0 |