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Answer: The data contain outliers.
**Correct Answer: C** **Explanation:** C is correct. There are at least two situations where the bootstrap will not work well: if there are outliers in the data, and if the data are dependent on one another. A is incorrect. One benefit of the bootstrap is the fact that they do not have distributional requirements – the bootstrap does not care what distribution the data come from. B is incorrect. The very opposite is true; data must be independent in order for the bootstrap to be effective. D is incorrect. The same explanation for A applies here. **Section:** Quantitative Analysis **Reference:** Global Association of Risk Professionals. Quantitative Analysis. New York, NY: Pearson, 2019. Chapter 13. Simulation and Bootstrapping. **Learning Objective:** Describe situations where the bootstrapping method is ineffective.
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A modeling team at a risk management consulting firm is debating whether it is appropriate to use the bootstrap technique to analyze a particular sample of data. Which of the following represents a situation where the bootstrap technique will be ineffective?
A
The data follow an asymmetric distribution.
B
The data are independent and identically distributed.
C
The data contain outliers.
D
The data are normally distributed.
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