Timeline for Using model with heteroskedasticity for predictions?
Current License: CC BY-SA 3.0
8 events
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| Sep 20, 2017 at 3:20 | comment | added | Jesse | Thanks for the comments. I saw the t-test and assumed statistical inference was in order, not model prediction (should have read all the text!). Yes, Matthew is right--heteroscedasticity will not influence a model's predictive accuracy; homoscedasticity is only necessary for consistency and unbiasedness. | |
| Sep 20, 2017 at 3:16 | history | edited | Jesse | CC BY-SA 3.0 |
wrong method
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| Sep 19, 2017 at 18:31 | comment | added | Matthew Drury | Yup, homoskedacity does not infringe on the predictive capabilities of linear regression. There may be other, better models to use, due to either better capturing non-linear effects (ala logistic regression), or using the data more efficiently, but the concerns that lead to the homoskedactiy "assumption" are non-predictive in nature. | |
| Sep 19, 2017 at 17:20 | comment | added | zugunru | Thank you for the replies! @MatthewDrury are you saying you think the model would be ok to use for predictions? Dimitriy Masterov, I'm not sure if you are saying I don't need to assume homoskedasticity (in which case I'm not sure what type of regression you mean) or that I can adjust the model itself, which is what I'm asking for technical advice on. I am posing this question because I've found plenty on how to find the robust standard errors, but nothing on how I could actually apply them to the model in R. Thank you | |
| Sep 19, 2017 at 0:07 | comment | added | Matthew Drury | (-1) This is not correct. Linear regression is still a consistent estimator of the conditional mean even when errors are heteroskedastic. Also, the phrase "This problem might be better solved using gradient descent instead of regression" makes no sense to me. That's an apples an oranges comparison. Linear regression is a statistical/ML model, gradient descent is an optimization procedure. | |
| Sep 18, 2017 at 22:50 | comment | added | dimitriy | The assumption of homoskedasticity can usually be relaxed with regression quite easily. | |
| Sep 18, 2017 at 21:28 | review | First posts | |||
| Sep 19, 2017 at 0:16 | |||||
| Sep 18, 2017 at 21:28 | history | answered | Jesse | CC BY-SA 3.0 |