Timeline for Best Choice of Estimator. How to compute Variance of Estimator.Basis for it?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 11, 2020 at 14:29 | comment | added | Erin Sprünken | I'd recommend opening another question for this since other people may have a different view or more experience. However, If they're both unbiased and equal variances, then I'd choose the one which is easier to compute (or faster to compute on a PC). Although, what I teach to my students, is that unbiasedness is not the non-plus-ultra. It is really nice to have and sometimes important. However, there are cases where a biased estimator has a lower MSE. Just as an additional information. | |
| Apr 11, 2020 at 4:30 | vote | accept | MSIS | ||
| Apr 11, 2020 at 4:29 | comment | added | MSIS | Erin, I selected your answer. One last question, please : If we have two estimators E1, E2 of the same parameter and they are both unbiased and they have the same variance/s.e, what criteria would we use to select one over the other. Or maybe that should be a separate question? | |
| Apr 11, 2020 at 4:26 | vote | accept | MSIS | ||
| Apr 11, 2020 at 4:30 | |||||
| Apr 8, 2020 at 23:47 | comment | added | Erin Sprünken | That's true. But the exercise you are working on is of typical nature where draws of the population are supposedly stochastically independent. If you are interested in getting the expectation of X_i^2 I suggest to look at the theoretical variance by integration and reformulate that. Then you'll see that E[X_i^2] is Var(X) + mu^2. | |
| Apr 8, 2020 at 23:43 | comment | added | MSIS | Thank you Erin. I guess we need indepence to use this formula, otherwise we need to use covariance/correlation, right? | |
| Apr 8, 2020 at 23:18 | history | answered | Erin Sprünken | CC BY-SA 4.0 |