Explanation:
In the context of bootstrapping, re-sampling variability refers to the error that arises from the fact that we take only a finite number of bootstrap re-samples (denoted by B) from our original sample, rather than an infinite number. This limitation is inherent to the bootstrapping process, as it is practically impossible to take an infinite number of re-samples. As a result, our bootstrap estimates are subject to a certain degree of variability that is directly related to the number of re-samples taken. The larger the number of re-samples, the lower the re-sampling variability, and vice versa. However, even with a large number of re-samples, the re-sampling variability can never be completely eliminated, thus contributing to the overall error in the bootstrap estimates.
Choice A is incorrect. Un-sampling variability is not a recognized term in the context of bootstrapping or statistical analysis. It does not represent any type of error associated with bootstrapping.
Choice C is incorrect. Dual-sampling variability, similar to un-sampling variability, is not a recognized term in statistics or bootstrapping methodology. It does not denote any form of error that can occur during the process of bootstrapping.
Choice D is incorrect. While it may seem plausible due to the use of the term 'bootstrapping', Bootstrapping variability isn't an identified type of error in statistical analysis or bootstrapping procedures. The errors associated with bootstrap methods are more accurately described as re-sampling errors (as stated in option B), rather than 'bootstrapping variability'.
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Q.1495 Even though bootstrapping has numerous advantages, the bootstrap estimates are associated with a little bias or error. Which of the following presents an error of bootstrapping?
A
Un-sampling variability.
B
Re-sampling variability.
C
Dual-sampling variability.
D
Bootstrapping variability.