Kernel regression
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The Kernel regression is a non-parametrical techniques in statistics to estimate the conditional expectation of random variable.
In any non-parametric regression, the conditional expectation of a variable relative to a variable may be written:
where is a non-parametric function.
Nadarya (1964) and Watson (1964) proposed to estimate as a locally weighted average, using a kernel as a weighting function. The Nadarya-Watson estimator is:
where is a kernel with a bandwith .