Timeline for On the importance of the i.i.d. assumption in statistical learning
Current License: CC BY-SA 3.0
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Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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May 25, 2016 at 8:56 | comment | added | Quantuple | thanks a lot for this additional info on cross-validation for non-iid data. | |
May 24, 2016 at 18:56 | history | edited | Tim | CC BY-SA 3.0 |
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May 24, 2016 at 18:50 | comment | added | Tim | @Quantuple check classic "An Introduction to the Bootstrap" by Efron and Tibshirani and "Bootstrap Methods and Their Application" by Davison and Hinkley to read about bootstrap (same ideas apply to cross-validation); time-series handbooks describe how to use cross-validation and bootstrap for such data (i.e. one step ahead cross-validation). Check also my edit. | |
May 24, 2016 at 18:47 | history | edited | Tim | CC BY-SA 3.0 |
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May 24, 2016 at 9:41 | comment | added | Quantuple | Thanks again. I indeed remember having read somewhere about such techniques. Is there a source which discusses all potential candidate methods? I've just stumbled upon the paper by C. Bergmeir, R. Hyndman, B. Koo "A note on the Validity of Cross-Validation for Evaluating Time Series Prediction" which I will try to read asap. | |
May 24, 2016 at 9:26 | comment | added | Tim | @Quantuple then you use methods for non i.i.d. data, e.g. in time-series sample whole blocks of data in bootstrap etc | |
May 24, 2016 at 8:22 | comment | added | Quantuple | (ctd) ... In other words, although your answer definitely sheds some light on the iid concept, I would like to know more on a technical basis: when this is violated, what are the effects? | |
May 24, 2016 at 8:22 | comment | added | Quantuple | I appreciate that you took some time to answer my concerns. While you provided a really nice explanation of what the iid assumption conveys... it leaves me frustrated. (1) For training the LASSO $y_i \vert {\bf{X}}_i$ is enough (since it allows one to write the penalised log-likelihood estimation), but what is the impact of $\bf{X}_i$ not being an iid sample (which is the case if predictors come from a time-series and are hence autocorrelated). (2) Also what is the result of not having exchangeability on the use of cross-validation for instance? (ctd) ... | |
May 24, 2016 at 6:37 | history | edited | Tim | CC BY-SA 3.0 |
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May 23, 2016 at 20:58 | history | answered | Tim | CC BY-SA 3.0 |