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]
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.
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(SP 16.1) Definitions: Estimator, Bias and Mean Squared Error (MSE)
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Estimating the Mean Squared Error (Module 2 1 8)
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❖ Integration by Parts Made Easy! ❖
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See also
References
- ^ Wand, M. P.; Jones, M. C. (1994). Kernel smoothing. CRC press. p. 15.