Mean integrated squared error
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In statistics, the mean integrated squared error (MISE) is used in density estimation. The MISE of an estimate of an unknown probability density is given by[1]
- [math]\displaystyle{ \operatorname{E}\|f_n-f\|_2^2=\operatorname{E}\int (f_n(x)-f(x))^2 \, dx }[/math]
where ƒ is the unknown density, ƒn is its estimate based on a sample of n independent and identically distributed random variables. Here, E denotes the expected value with respect to that sample.
The MISE is also known as L2 risk function.
See also
References
- ↑ Wand, M. P.; Jones, M. C. (1994). Kernel smoothing. CRC press. pp. 15.
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