meta user
network profile
| bio | website | |
|---|---|---|
| location | ||
| age | 35 | |
| visits | member for | 2 years |
| seen | 13 hours ago | |
| stats | profile views | 12 |
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Oct 19 |
asked | Constructing multilevel regression design matrix |
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Aug 21 |
comment |
Estimating maximum predictive power in noisy data Bayes error is the right theoretical view, but as noted we cannot know this in real data. There are approaches that try to estimate the noise ceiling from real data, e.g., Quantifying variability in neural responses and its application for the validation of model predictions. Network. 2004. Hsu A, Borst A, Theunissen FE.[link] (ahsu.psychol.ucl.ac.uk/ahsu/papers/quantifying_variability.PDF) |
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Aug 21 |
accepted | Estimating maximum predictive power in noisy data |
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Aug 20 |
awarded | Tumbleweed |
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Aug 13 |
asked | Estimating maximum predictive power in noisy data |
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May 20 |
comment |
Transformations of input variables to linearize a regression function Mapping to the cluster mean (numeric) is one possibility. Mapping to cluster ID (discrete category) is another. |
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May 18 |
revised |
Transformations of input variables to linearize a regression function corrected spelling |
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May 18 |
comment |
Transformations of input variables to linearize a regression function The terminology may differ between fields; for example, interaction terms could be thought of as 2nd order terms in a Volterra series expansion, which is a nonlinear transformation of the inputs. The response is modeled as a linear combination of the output of the nonlinear basis functions. |
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May 18 |
awarded | Editor |
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May 18 |
comment |
Transformations of input variables to linearize a regression function agreed, I clarified the question asking for any specific examples used in practice |
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May 18 |
revised |
Transformations of input variables to linearize a regression function clarify question |
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May 18 |
asked | Transformations of input variables to linearize a regression function |
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May 17 |
asked | Standard error of parameter estimates in regularized regression |
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Nov 28 |
awarded | Scholar |
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Nov 28 |
accepted | Multiple comparisons for parameter significance in a sparse high dimensional regression model |
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Nov 7 |
awarded | Supporter |
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Nov 1 |
awarded | Student |
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Nov 1 |
asked | Multiple comparisons for parameter significance in a sparse high dimensional regression model |
