Timeline for Conditional Variance is the Best Predictor for Which Loss Function?
Current License: CC BY-SA 3.0
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Nov 25, 2023 at 16:34 | comment | added | picky_porpoise | If there is no a priori knowledge of the mean, it is possible to prove that no loss function exists, see my answer to this question | |
Dec 18, 2020 at 8:14 | vote | accept | Cagdas Ozgenc | ||
Jun 26, 2017 at 7:22 | comment | added | Cagdas Ozgenc | @MichaelChernick There is nothing confusing about it. I provided the answer below, albeit with some restrictions (which were actually conjectured as part of the question). | |
Jun 26, 2017 at 7:14 | answer | added | Cagdas Ozgenc | timeline score: 4 | |
Jan 25, 2017 at 15:46 | history | edited | Cagdas Ozgenc | CC BY-SA 3.0 |
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Dec 19, 2016 at 19:39 | history | edited | Cagdas Ozgenc | CC BY-SA 3.0 |
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Dec 18, 2016 at 19:31 | history | edited | Cagdas Ozgenc | CC BY-SA 3.0 |
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Dec 18, 2016 at 10:04 | history | edited | Cagdas Ozgenc | CC BY-SA 3.0 |
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Dec 18, 2016 at 9:25 | history | edited | Cagdas Ozgenc | CC BY-SA 3.0 |
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Dec 18, 2016 at 1:48 | comment | added | Michael R. Chernick | The question is confusing. First you say you want to predict Y given X and point out correctly that for squared error loss it is the best predictor. But then you pick VAR[Y|X]. Are you still trying to predict Y? | |
Dec 17, 2016 at 22:08 | history | asked | Cagdas Ozgenc | CC BY-SA 3.0 |