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Erschienen in:
2021 | OriginalPaper | Buchkapitel
9. Resampling Techniques
verfasst von : Raosaheb Latpate, Jayant Kshirsagar, Vinod Kumar Gupta, Girish Chandra
Erschienen in: Advanced Sampling Methods
Verlag: Springer Singapore
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Abstract
The classical theory of statistical inference involves estimating a parameter (or a parametric function) and then determining precision of the estimate using a random sample. An estimator can be obtained that have the specified desirable properties (like unbiasedness, minimum variance, etc.), but there is a conceptual problem with the method of determining precision of the estimate. Conceptually, precision is defined as a measure of variability of the estimator in terms of the variation in the value of the estimator from sample to sample. However, in practice, it is determined using observations in a single sample only. Ideally, precision of an estimator should be determined using values of the estimator from several random samples. This apparent discrepancy in the concept and practice is overcome by the method of resampling. Resampling methods are based on the principle of treating the observed sample as the target population and then sampling it repeatedly with replacement.