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In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y.
Statistical implementation, Nadaraya–Watson kernel regression & Related
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Kernel regression | is a | non-parametric technique to estimate the conditional expectation of a random variable | 0.90 | text |
| Kernel regression | related to External links | Scale-adaptive | 0.60 | section |
| Kernel regression | related to External links | Matlab | 0.60 | section |
| Kernel regression | related to External links | Tutorial | 0.60 | section |
| Kernel regression | related to External links | Kernel | 0.60 | section |
| Kernel regression | related to External links | Microsoft Excel | 0.60 | section |
| Kernel regression | related to External links | An | 0.60 | section |
| Kernel regression | related to External links | Requires | 0.60 | section |
| Kernel regression | related to External links | NET | 0.60 | section |
| Kernel regression | related to External links | Python | 0.60 | section |
| Kernel regression | related to Related | According | 0.60 | section |
| Kernel regression | related to Related | David Salsburg | 0.60 | section |
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