Timeline for What is the difference between fixed effect, random effect in mixed effect models?
Current License: CC BY-SA 2.5
6 events
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| Apr 2, 2017 at 8:41 | comment | added | SmallChess | This is in contrast to @ben answer. I believe the answer is wrong. | |
| May 2, 2016 at 17:18 | comment | added | conjugateprior | @rolando2 In any case, this is simply false. Specifically, for Bayesians the parameters are whatever kind of thing the theory / likelihood says they are. Only one's uncertainty about what values they take is represented using probability distributions. Consequently sometimes the parameters are modeled as fixed and unknown ('fixed') and sometimes as coming from a distribution ('random') though the latter device is often motivated by an exchangeability judgement rather than a belief about a sampling process. | |
| Jun 1, 2014 at 22:15 | review | Low quality posts | |||
| Jun 1, 2014 at 22:17 | |||||
| Dec 29, 2013 at 10:09 | review | Suggested edits | |||
| Dec 29, 2013 at 10:24 | |||||
| Jan 27, 2012 at 0:48 | comment | added | rolando2 | Interesting. But since fixed or random can be considered a condition of a given variable (a given column of data) rather than of a parameter associated with that variable,...does your answer fully apply? | |
| Nov 21, 2010 at 18:00 | history | answered | Shige | CC BY-SA 2.5 |