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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.
In any nonparametric regression, the conditional expectation of a variable Y {\displaystyle Y} relative to a variable X {\displaystyle X} may be written:
E ( Y ∣ X ) = m ( X ) {\displaystyle \operatorname {E} (Y\mid X)=m(X)}
where m {\displaystyle m} is an unknown function.
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