Mean Integrated Squared Error (MISE) is a measure used to assess the performance of an estimator, particularly in non-parametric statistics, by evaluating the average squared difference between the estimated density function and the true density function across a specified domain. It provides insight into how well the estimator approximates the underlying distribution, making it crucial in contexts like kernel density estimation where accurate density estimation is essential for data analysis and interpretation.
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